Journal articles on the topic 'Chess – Mathematical models'
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Balasubramaniam, Ramesh, and Anatol G. Feldman. "Some robotic imitations of biological movements can be counterproductive." Behavioral and Brain Sciences 24, no. 6 (December 2001): 1050–51. http://dx.doi.org/10.1017/s0140525x01220123.
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It is proposed here that Webb's ideas about robots as possible models of animals need some rethinking. In our view, even though widely used biorobotics strategies are fairly successful at reproducing the macroscopic behavior of biological systems, there are still several problems unresolved on the side of robotics as well as biology. Both mathematical and hardware-like robotics models should be feasible physiologically. Control principles elaborated in robotics are not necessarily applied to biological control systems. Although observations of flying birds inspired aerodynamics and thus modern airplanes, little knowledge has been added to the neurophysiological principles underlying flight in birds. Chess playing computers might outperform most chess players, but they cannot be considered as physiologically feasible models of human thinking.
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Stempin, Agnieszka. "NAJSTARSZE (XI-XII W.) FIGURY SZACHOWE WYKONANE W STYLISTYCE ABSTRAKCYJNEJ ARABSKIEJ Z TERENU POLSKI , NA TLE TRENDÓW EUROPEJSKICH ZWIĄZANYCH Z POCZĄTKOWYMI FAZAMI ADAPTACJI W EUROPIE." Slavia Antiqua. Rocznik poświęcony starożytnościom słowiańskim, no. 62 (November 8, 2021): 285–315. http://dx.doi.org/10.14746/sa.2021.62.12.
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Chess is a board game, in the Middle Ages referred to as a tabula. During the long way it took since its origin in India in the 6th century until modern times, the subsequent communities left their own, inimitable cultural marks. In India, chess had a deeply mystical nature; Persians used chess to picture the world as a battlefield; Arabs systematised many concepts and took note of the mathematical aspect; Europe made use of chess to define rules that should apply to an ideal society. This shows a perfect understanding of the balance on the chessboard, the mutual dependencies and consistent actions leading to success – both when playing and creating social life. Medieval literature provides an excellent basis for studies of the intertwining cultural trends and describing the reality. In the literature, elements based on playing chess are oftentimes among the postulated modes of education. However, the ideas encountered by the potential users of chess tournaments were best communicated by the figures and the accumulated plethora of notions. An analysis of the changes affecting jackstraws at an early stage of the game’s adaptation in Europe and other territories which took over chess as cultural models, leads to a conclusion that the material from the 11th-12th centuries that comes from Polish collections matches many Latin trends and shows considerable knowledge thereof.
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Drabik, Ewa. "Several Remarks on the Role of Certain Positional and Social Games in the Creation of the Selected Statistical and Economic Applications." Foundations of Management 8, no. 1 (January 1, 2016): 289–96. http://dx.doi.org/10.1515/fman-2016-0022.
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Abstract The game theory was created on the basis of social as well as gambling games, such as chess, poker, baccarat, hex, or one-armed bandit. The aforementioned games lay solid foundations for analogous mathematical models (e.g., hex), artificial intelligence algorithms (hex), theoretical analysis of computational complexity attributable to various numerical problems (baccarat), as well as illustration of several economic dilemmas - particularly in the case where the winner takes everything (e.g., noughts and crosses). A certain gambling games, such as a horse racing, may be successfully applied to verify a wide spectrum of market mechanism, for example, market effectiveness or customer behavior in light of incoming information regarding a specific product. One of a lot applications of the slot machine (one-armed bandit) is asymptotically efficient allocation rule, which was assigned by T.L. Lai and H. Robbins (1985). In the next years, the rule was developed by another and was named a multi-armed. The aim of the paper is to discuss these social games along with their potential mathematical models, which are governed by the rules predominantly applicable to the social and natural sciences.
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Zhou, Kun, Wenyong Wang, Teng Hu, and Kai Deng. "Application of Improved Asynchronous Advantage Actor Critic Reinforcement Learning Model on Anomaly Detection." Entropy 23, no. 3 (February 25, 2021): 274. http://dx.doi.org/10.3390/e23030274.
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Anomaly detection research was conducted traditionally using mathematical and statistical methods. This topic has been widely applied in many fields. Recently reinforcement learning has achieved exceptional successes in many areas such as the AlphaGo chess playing and video gaming etc. However, there were scarce researches applying reinforcement learning to the field of anomaly detection. This paper therefore aimed at proposing an adaptable asynchronous advantage actor-critic model of reinforcement learning to this field. The performances were evaluated and compared among classical machine learning and the generative adversarial model with variants. Basic principles of the related models were introduced firstly. Then problem definitions, modelling processes and testing were detailed. The proposed model differentiated the sequence and image from other anomalies by proposing appropriate neural networks of attention mechanism and convolutional network for the two kinds of anomalies, respectively. Finally, performances with classical models using public benchmark datasets (NSL-KDD, AWID and CICIDS-2017, DoHBrw-2020) were evaluated and compared. Experiments confirmed the effectiveness of the proposed model with the results indicating higher rewards and lower loss rates on the datasets during training and testing. The metrics of precision, recall rate and F1 score were higher than or at least comparable to the state-of-the-art models. We concluded the proposed model could outperform or at least achieve comparable results with the existing anomaly detection models.
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ROZIKOV, U. A., and Y. M. SUHOV. "GIBBS MEASURES FOR SOS MODELS ON A CAYLEY TREE." Infinite Dimensional Analysis, Quantum Probability and Related Topics 09, no. 03 (September 2006): 471–88. http://dx.doi.org/10.1142/s0219025706002494.
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We consider a nearest-neighbor solid-on-solid (SOS) model, with several spin values 0, 1,…, m, m ≥ 2, and zero external field, on a Cayley tree of order k (with k + 1 neighbors). The SOS model can be treated as a natural generalization of the Ising model (obtained for m = 1). We mainly assume that m = 2 (three spin values) and study translation-invariant (TI) and "splitting" (S) Gibbs measures (GMs). (Splitting GMs have a particular Markov-type property specific for a tree.) Furthermore, we focus on symmetric TISGMs, with respect to a "mirror" reflection of the spins. [For the Ising model (where m = 1), such measures are reduced to the "disordered" phase obtained for free boundary conditions, see Refs. 9, 10.] For m = 2, in the antiferromagnetic (AFM) case, a symmetric TISGM (and even a general TISGM) is unique for all temperatures. In the ferromagnetic (FM) case, for m = 2, the number of symmetric TISGMs and (and the number of general TISGMs) varies with the temperature: this gives an interesting example of phase transition. Here we identify a critical inverse temperature, [Formula: see text] such that [Formula: see text], there exists a unique symmetric TISGM μ* and [Formula: see text] there are exactly three symmetric TISGMs: [Formula: see text] (a "bottom" symmetric TISGM), [Formula: see text] (a "middle" symmetric TISGM) and [Formula: see text] (a "top" symmetric TISGM). For [Formula: see text] we also construct a continuum of distinct, symmertric SGMs which are non-TI. Our second result gives complete description of the set of periodic Gibbs measures for the SOS model on a Cayley tree. A complete description of periodic GMs means a characterisation of such measures with respect to any given normal subgroup of finite index in the representation group of the tree. We show that (i) for an FM SOS model, for any normal subgroup of finite index, each periodic SGM is in fact TI. Further, (ii) for an AFM SOS model, for any normal subgroup of finite index, each periodic SGM is either TI or has period two (i.e. is a chess-board SGM).
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Kaminska, Marianna, Valerii Degtuar, and Oleksandr Yaresko. "Mathematical modeling of the chest, its funnel-shaped deformation and thoracoplasty." ORTHOPAEDICS, TRAUMATOLOGY and PROSTHETICS, no. 2 (October 12, 2021): 17–22. http://dx.doi.org/10.15674/0030-59872021217-22.
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The most common method of treating of the congenital funnel-shaped chest is thoracoplasty method by D. Nuss. During this surgery, a significant mechanical effect is created on the ribs, sternum, spinal column, which act instantly and continuously for a long time and create new biomechanical conditions for the «chest – rib – spine» system. Objective. To construct a functional model of the chest with a spinal column, which takes into account the movements in the costal-vertebral joints, it allows modeling the funnel-shaped deformation in conditions close to the reality, its operative correction, predicting the results and choosing the optimal parameters of thoracoplasty. Methods. Normal and funnel-shaped chest models based on the articular connection of the ribs to the spine were created using SolidWorks. The main calculations were made using the ANSYS program. To estimate the stress-strain state (SSS), stresses are selected by Mises. Results. The created dynamic mathematical model of the chest makes it possible to conduct a reliable analysis of the biomechanical interaction of the plate with the chest, to analyze the stress-strain state of the constructed models in the norm, with and without taking into account the movements in the costal-vertebral joints. In addition, it allows to simulate the operation by D. Nuss and to study the biomechanical changes in conditions close to reality, occurring in the «chest – rib – spine» system, to determine the areas of maximum loads and safety boundaries. Conclusions. The reproduction of articular ribs rotation in the dynamic model changes the picture of the SSS distribution. In the case of modeling the correction of funnel-shaped deformation of the chest by the method by D. Nuss, the largest zone of stress concentration was found on the outer posterior surface of the sixth pair of ribs. The most tense vertebrae were ThV– ThVI, but the maximum values did not exceed the permissible values. In the case of a lower plate conduction, the correction is achieved with better SSS values in the higher elements of the «chest – ribs – spine» system.
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Kozlov, V. A., O. Yu Dmitrieva, G. P. Itkin, A. S. Ivanov, A. P. Kuleshov, E. A. Volkova, and T. N. Govorova. "The optimization of an accomodation in the thoracic cavity child the axial pump don-3 (the mathematical model research)." Russian Journal of Transplantology and Artificial Organs 20, no. 3 (September 17, 2018): 40–44. http://dx.doi.org/10.15825/1995-1191-2018-3-40-44.
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We have developed and tested a technique for constructing 3D models of the chest and thoracic cavity organs. Due to the obtained results, the mathematical model was successfully used in the development of classification of variants of placement of implantable auxiliary circulation systems, and also for building 3D models for other purposes. In particular, the patients were graded according to the variants of placing the children’s axial pump DON-3 inside the patient’s chest cavity. Based on the data, the first fitting of the DON-3 pump was performed on a patient aged 7 years.
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Dihtiar, V. A., M. O. Kaminska, and O. V. Yaresko. "Mathematical calculation and coefficient value of chest shape recovery for planning thoracoplasty of pectus excavatum." TRAUMA 22, no. 1 (April 9, 2021): 26–32. http://dx.doi.org/10.22141/1608-1706.1.22.2021.226408.
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Background. Pectus excavatum is characterized by retraction of the sternum and anterior ribs of different depth and width. The formation, its prediction, calculation of chest deformity, and their study when planning thoracoplasty using the Nuss procedure for this pathology is an important problem of orthopedics and thoracic surgery. The purpose of the work was to calculate the coefficient of restoration of the chest shape by the ratio of the pectus excavatum depth and the chest size in the frontal plane before and after mathematical modeling of thoracoplasty using the Nuss procedure. Methods. To assess displacement of ribs depen-ding on depth deformity of chest h, two models were built. The first model is a flat frame on supports, the elements of which consist of cartilaginous parts of ribs and sternum. For this model, the dependence of the force F was determined, which is necessary to correct the depth of chest deformity. The second model is a curved bar that simulates a rib, to one of the ends of which a support load is applied, calculated during the analysis of the first model. For this model, the displacement of the plate fixation point under the action of a given force was determined. To obtain more accurate results, a finite element study was performed on a chest model. Results. The correction of pectus excavatum depth without fixing plate to ribs was simulated. The displacements of rib sections in the place of plate fixation at different depths of pectus excavatum was assessed: h = 2 cm, h = 3 cm, h = 4 cm, h = 5 cm. The analysis of calculation results showed that after correction of the depth of chest deformity, its size in the frontal plane decreases. So, at the maximum deformation depth h = 5 cm, the deviation of the rib sections at the plate fixation point occurred by 2.4 cm. Conclusions. The relationship between the pectus excavatum depth and chest size in the frontal plane was established when modeling the newly formed chest form during for Nuss procedure. The coefficient of restoring the chest shape was mathematically calculated, which is 2 (2∆ = h), where h is the depth of pectus excavatum. The practical significance of the coefficient is that when planning thoracoplasty and shaping plate, the distance between its lateral ends, which corresponds to the chest shape and adjoin ribs, must be reduced by ½ h (where h is the depth of pectus excavatum) before correcting the pectus excavatum full adherence to the ribs in the postoperative period.
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Dillard, Elizabeth, Fred A. Luchette, Benjamin W. Sears, John Norton, Carol R. Schermer, R. Lawrence Reed, Richard L. Gamelli, and Thomas J. Esposito. "Clinician vs mathematical statistical models: which is better at predicting an abnormal chest radiograph finding in injured patients?" American Journal of Emergency Medicine 25, no. 7 (September 2007): 823–30. http://dx.doi.org/10.1016/j.ajem.2006.12.009.
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Nair, Vivek, Samuel Auger, Sara Kochanny, Frederick Howard, Daniel Ginat, Olga Pasternak-Wise, Aditya Juloori, et al. "Mathematical predication models to optimize post-treatment surveillance in HPV-associated oropharyngeal cancer." Journal of Clinical Oncology 39, no. 15_suppl (May 20, 2021): 6027. http://dx.doi.org/10.1200/jco.2021.39.15_suppl.6027.
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6027 Background: In this study we develop post-treatment imaging surveillance schedules for locally advanced oropharyngeal carcinoma (OPC) specific to the unique recurrence patterns of tumor stage and HPV status, using mathematical models. Current post-treatment imaging surveillance recommendations for OPC are not evidence based. The exception is the use of a positron emission tomography (PET) scan at 3 months post-treatment, after which practice across institutions diverge. An optimized and personalized surveillance schedule for OPC patients can minimize costs and diagnostic delays. Methods: A Markov multi-state model defining local and distant recurrences was trained using 2159 patients from the National Cancer Database. Patients from 2010-2015 treated at an academic or major cancer center with curative radiotherapy were included. Tumors must have been stage III to IVB (AJCC 7th edition) with known p16/HPV status. Model performance was then successfully externally validated using the 2016 International Collaboration on Oropharyngeal cancer Network for Staging (ICON-S) study. Optimized radiographic surveillance schedules were created using this model, assuming a PET at month 3 and including 0 to 6 additional computed tomography (CT) scans of the neck and chest. Optimization was done for minimization of latency, defined as time between disease recurrence and radiographic discovery. Results: Model-selected schedules varied significantly from commonly utilized-surveillance schedules (such as imaging every 3 months within the first year from treatment) and showed lower mean diagnostic latency for every stage and HPV status (shown in Table). In the lowest risk cohort (Stage III HPV+), the optimized schedule had a sensitivity of 65% and latency of 3.1 months. In the highest risk group (Stage IVB HPV-), the optimized schedule had a sensitivity of 76% and latency of 1.9 months. Conclusions: Mathematical model optimization for HPV status and stage is feasible and produces non-intuitive results. These results could be used to inform surveillance if payors reimburse for fewer total scans. Across all cohorts, each added CT scan increases surveillance sensitivity and decreases latency. Incorporation of physical exam and direct visualization results into the model are still needed. Future steps include cost effectiveness research and prospective clinical trials.[Table: see text]
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Канунникова, A. Kanunnikova, Ивахно, N. Ivakhno, Федоров, and S. Fedorov. "Mathematical modeling of processes in the human respiratory system." Journal of New Medical Technologies. eJournal 9, no. 2 (July 6, 2015): 0. http://dx.doi.org/10.12737/11436.
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Scientific relevance and purpose. This research looks at the urgent task of modeling the structure of the human respiratory system and processes occurring in it, in order to predict the changes in physiological parameters occurring under different mechanical actions.
Results. This paper suggests mathematical model based on the description of equations of the mass flow and mass flow rate in the pulmonary channels in cases, when airways are branched in accordance with the prin-ciple of regular dichotomy with regard to the equations of work dynamics of the respiratory muscles and the ability to model different stresses in the breathing circuit, caused by trainers. The research examined the stresses generated by muscles in the radial and axial direction of the equivalent hollow cylinder, which represented the chest with regard to the elastic stress component in the cylinder wall and variable muscle stress in the circumfe-rential direction. The paper contains the results of mathematical modeling of breathing without stress, the graphs of volume and mass flow in lungs generations and pressure-flow diagram.
Conclusions. The developed mathematical models enable more precise multi-parameter modeling of the dynamics of functioning of complex biotech system "respiratory muscles trainer - human", which enables the implementation of the prediction of shifts of physiological and mechanical properties from the values of the normal process and to adjust the control actions on this basis
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Malhotra, Priyanka, Sheifali Gupta, and Deepika Koundal. "Computer Aided Diagnosis of Pneumonia from Chest Radiographs." Journal of Computational and Theoretical Nanoscience 16, no. 10 (October 1, 2019): 4202–13. http://dx.doi.org/10.1166/jctn.2019.8501.
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Pneumonia is a deadly chest disease and is a major culprit behind numerous deaths every year. Chest radiographs (CXR) are commonly used for quick and cheap diagnosis of chest diseases. The interpretation of CXR’s for diagnosing pneumonia is difficult. This has created an interest in computer-aided diagnosis (CAD) for CXR images. In this study, a brief review of literature based on computer aided analysis of chest radiograph images for identification of pneumonia using different machine learning and deep learning models is presented and a comparison of these different techniques has been provided. In addition, the study also presents various publicly available chest X-ray data sets for training, testing and validation of deep learning models.
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Jee, Gaurav, GM Harshvardhan, and Mahendra Kumar Gourisaria. "Juxtaposing inference capabilities of deep neural models over posteroanterior chest radiographs facilitating COVID-19 detection." Journal of Interdisciplinary Mathematics 24, no. 2 (January 12, 2021): 299–325. http://dx.doi.org/10.1080/09720502.2020.1838061.
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Dovgalyuk, I. F., D. A. Kudlay, and A. A. Starshinova. "Tuberculosis prevalence in children in the Northwestern Federal District of Russia before and after COVID-19 pandemic: prognosis and epidemiological models." Pacific Medical Journal, no. 4 (January 17, 2023): 43–48. http://dx.doi.org/10.34215/1609-1175-2022-4-43-48.
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Aim. To assess changes in the epidemic indicators of tuberculosis infection (TB) in children in the Northwestern Federal District of Russia before and after the COVID-19 pandemic based on mathematical modeling and forecasting.Materials and methods. The main epidemiological indicators of TB were analyzed using the official statistical data for 2009–2021. A mathematical forecasting of epidemiological indicators was performed based on chest X-ray screening for TB. A statistical analysis was carried out using the software environment R (v.3.5.1) and the commercial software Statistical Package for Social Sciences (SPSS Statistics for Windows, version 24.0, IBM Corp., 2016). Time series forecasting was performed using the programming language of statistical calculations R, version 4.1.2 and the bsts package, version 0.9.8. Results. The mean regression coefficient of a single predictor was found to differ in a model for TB morbidity in children is 0.0098. X-ray screening for TB was established to be a significant mortality predictor in children. At least 60% of the population should undergo TB screening in order for TB prevalence to be controlled in a country with a population above 140 million people.Conclusions. The conducted study revealed a positive correlation between the incidence of tuberculosis in children in Russia and TB screening in at least 60% of the population. Under the current TB screening system in Russia, the epidemic TB situation will continue to improve, despite COVID-19 restrictions. At the same time, in the Northwestern Federal District of Russia, preventive TB screening can be considered sufficient only in the Kaliningrad, Murmansk, and Pskov Oblasts.
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Et. al., G. Jignesh Chowdary,. "Impact Of Machine Learning Models In Pneumonia Diagnosis With Features Extracted From Chest X-Rays Using VGG16." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 5 (April 11, 2021): 1521–30. http://dx.doi.org/10.17762/turcomat.v12i5.2119.
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Pneumonia is a viral, bacterial, or fungal infection that leads to the accumulation of pus or fluids in the alveoli of lungs causing breathlessness, lung abscess, or even death at later stages. Pneumonia is affecting a huge population across the globe. A quite large number of child deaths due to pneumonia are recorded which is significantly greater than death due to AIDS, malaria, and measles. Pneumonia diagnosis is considered one of the high priority research areas in Biomedicine. In this paper, a detailed comparative study was performed using various machine learning algorithms namely Random Forest (RF), Logistic Regression (LR), and Support Vector Machine (SVM). These models are trained with features extracted by a pre-trained deep convolutional neural network (DCNN), VGG16 for the diagnosis of pneumonia from chest x-rays. The combination of VGG16 along with Machine learning models witnessed a considerable improvement in accuracy with reduction in time consumed for training against the usage of DCNN models for prediction. The results of various machine learning models are fine-tuned by modifying the hyper parameters. By comparison, SVM with RBF kernel is identified to perform better than other classifiers.
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Lynnyk, M. I., I. V. Liskina, І. А. Kalabukha, V. I. Ignatieva, and O. R. Tarasenko. "Possibilities of radiomics in processing data of CT scan of the chest organs in diagnosis of pulmonary tuberculosis." Tuberculosis, Lung Diseases, HIV Infection, no. 2 (June 17, 2022): 36–40. http://dx.doi.org/10.30978/tb-2022-2-36.
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The article shows the possibility of applying radiomics in the processing of chest CT data in the diagnosis of pulmonary tuberculosis. Currently, a subjective method based on the knowledge and experience of a radiologist is used to process CT images. A new approach to CT image analysis can fundamentally change the diagnostic process. Its essence is to create mathematical models and computer algorithms that take medical images as input and produce pathophysiological features of tissues.Dragonfly software, provided free of charge by OBYECT RESERCH SYSTEMS (ORS), Montreal, Canada, is used for CT slice analysis, which enables segmentation, mathematical and statistical processing of images, construction of ordinary and segmented histograms. To work with the program, dicom - CT files are transformed into raster files (Tiff, Jpeg, Raw) and further analysis of CT slices is performed by grayscale gradations (behind image pixels, not behind dicom file voxels). It should be emphasized that the grayscale analysis correlates with the Hounsfield units.It has been shown that based on the data of pathomorphological examination of the affected tissue, it is impossible to determine the difference between chemoresistant and susceptible pulmonary tuberculosis.Processing of CT data with the construction of conventional and segmental histograms using Dragonfly software tools makes it possible to identify pathophysiological features of tissues in the diagnosis of sensitive and chemoresistant pulmonary tuberculosis. Further research is needed to identify patterns and differences in the determination of densities in the diagnosis of sensitive and chemoresistant pulmonary tuberculosis.
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Casha, Aaron R., Liberato Camilleri, Alexander Manché, Ruben Gatt, Daphne Attard, Marilyn Gauci, Marie-Therese Camilleri-Podesta, Stuart Mcdonald, and Joseph N. Grima. "Internal rib structure can be predicted using mathematical models: An anatomic study comparing the chest to a shell dome with application to understanding fractures." Clinical Anatomy 28, no. 8 (September 2, 2015): 1008–16. http://dx.doi.org/10.1002/ca.22614.
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Zhang, Sai, and Guo-Chang Yuan. "Deep Transfer Learning for COVID-19 Detection and Lesion Recognition Using Chest CT Images." Computational and Mathematical Methods in Medicine 2022 (October 15, 2022): 1–16. http://dx.doi.org/10.1155/2022/4509394.
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Starting from December 2019, the global pandemic of coronavirus disease 2019 (COVID-19) is continuously expanding and has caused several millions of deaths worldwide. Fast and accurate diagnostic methods for COVID-19 detection play a vital role in containing the plague. Chest computed tomography (CT) is one of the most commonly used diagnosis methods. However, a complete CT-scan has hundreds of slices, and it is time-consuming for radiologists to check each slice to diagnose COVID-19. This study introduces a novel method for fast and automated COVID-19 diagnosis using the chest CT scans. The proposed models are based on the state-of-the-art deep convolutional neural network (CNN) architecture, and a 2D global max pooling (globalMaxPool2D) layer is used to improve the performance. We compare the proposed models to the existing state-of-the-art deep learning models such as CNN based models and vision transformer (ViT) models. Based off of metric such as area under curve (AUC), sensitivity, specificity, accuracy, and false discovery rate (FDR), experimental results show that the proposed models outperform the previous methods, and the best model achieves an area under curve of 0.9744 and accuracy 94.12% on our test datasets. It is also shown that the accuracy is improved by around 1% by using the 2D global max pooling layer. Moreover, a heatmap method to highlight the lesion area on COVID-19 chest CT images is introduced in the paper. This heatmap method is helpful for a radiologist to identify the abnormal pattern of COVID-19 on chest CT images. In addition, we also developed a freely accessible online simulation software for automated COVID-19 detection using CT images. The proposed deep learning models and software tool can be used by radiologist to diagnose COVID-19 more accurately and efficiently.
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Biradar, Vidyadevi G., Mejdal A. Alqahtani, H. C. Nagaraj, Emad A. Ahmed, Vikas Tripathi, Miguel Botto-Tobar, and Henry Kwame Atiglah. "An Effective Deep Learning Model for Health Monitoring and Detection of COVID-19 Infected Patients: An End-to-End Solution." Computational Intelligence and Neuroscience 2022 (August 11, 2022): 1–14. http://dx.doi.org/10.1155/2022/7126259.
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The COVID-19 infection is the greatest danger to humankind right now because of the devastation it causes to the lives of its victims. It is important that infected people be tested in a timely manner in order to halt the spread of the disease. Physical approaches are time-consuming, expensive, and tedious. As a result, there is a pressing need for a cost-effective and efficient automated tool. A convolutional neural network is presented in this paper for analysing X-ray pictures of patients’ chests. For the analysis of COVID-19 infections, this study investigates the most suitable pretrained deep learning models, which can be integrated with mobile or online apps and support the mobility of diagnostic instruments in the form of a portable tool. Patients can use the smartphone app to find the nearest healthcare testing facility, book an appointment, and get instantaneous results, while healthcare professionals can keep track of the details thanks to the web and mobile applications built for this study. Medical practitioners can apply the COVID-19 detection model for chest frontal X-ray pictures with ease. A user-friendly interface is created to make our end-to-end solution paradigm work. Based on the data, it appears that the model could be useful in the real world.
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Yue, Zhenjia, Liangping Ma, and Runfeng Zhang. "Comparison and Validation of Deep Learning Models for the Diagnosis of Pneumonia." Computational Intelligence and Neuroscience 2020 (September 18, 2020): 1–8. http://dx.doi.org/10.1155/2020/8876798.
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As a respiratory infection, pneumonia has gained great attention from countries all over the world for its strong spreading and relatively high mortality. For pneumonia, early detection and treatment will reduce its mortality rate significantly. Currently, X-ray diagnosis is recognized as a relatively effective method. The visual analysis of a patient’s X-ray chest radiograph by an experienced doctor takes about 5 to 15 minutes. When cases are concentrated, this will undoubtedly put tremendous pressure on the doctor’s clinical diagnosis. Therefore, relying on the naked eye of the imaging doctor has very low efficiency. Hence, the use of artificial intelligence for clinical image diagnosis of pneumonia is a necessary thing. In addition, artificial intelligence recognition is very fast, and the convolutional neural networks (CNNs) have achieved better performance than human beings in terms of image identification. Therefore, we used the dataset which has chest X-ray images for classification made available by Kaggle with a total of 5216 train and 624 test images, with 2 classes as normal and pneumonia. We performed studies using five mainstream network algorithms to classify these diseases in the dataset and compared the results, from which we improved MobileNet’s network structure and achieved a higher accuracy rate than other methods. Furthermore, the improved MobileNet’s network could also extend to other areas for application.
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Shetty, Shashank, Ananthanarayana V. S., and Ajit Mahale. "MS-CheXNet: An Explainable and Lightweight Multi-Scale Dilated Network with Depthwise Separable Convolution for Prediction of Pulmonary Abnormalities in Chest Radiographs." Mathematics 10, no. 19 (October 5, 2022): 3646. http://dx.doi.org/10.3390/math10193646.
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Pulmonary diseases are life-threatening diseases commonly observed worldwide, and timely diagnosis of these diseases is essential. Meanwhile, increased use of Convolution Neural Networks has promoted the advancement of computer-assisted clinical recommendation systems for diagnosing diseases using chest radiographs. The texture and shape of the tissues in the diagnostic images are essential aspects of prognosis. Therefore, in the latest studies, the vast set of images with a larger resolution is paired with deep learning techniques to enhance the performance of the disease diagnosis in chest radiographs. Moreover, pulmonary diseases have irregular and different sizes; therefore, several studies sought to add new components to existing deep learning techniques for acquiring multi-scale imaging features from diagnostic chest X-rays. However, most of the attempts do not consider the computation overhead and lose the spatial details in an effort to capture the larger receptive field for obtaining the discriminative features from high-resolution chest X-rays. In this paper, we propose an explainable and lightweight Multi-Scale Chest X-ray Network (MS-CheXNet) to predict abnormal diseases from the diagnostic chest X-rays. The MS-CheXNet consists of four following main subnetworks: (1) Multi-Scale Dilation Layer (MSDL), which includes multiple and stacked dilation convolution channels that consider the larger receptive field and captures the variable sizes of pulmonary diseases by obtaining more discriminative spatial features from the input chest X-rays; (2) Depthwise Separable Convolution Neural Network (DS-CNN) is used to learn imaging features by adjusting lesser parameters compared to the conventional CNN, making the overall network lightweight and computationally inexpensive, making it suitable for mobile vision tasks; (3) a fully connected Deep Neural Network module is used for predicting abnormalities from the chest X-rays; and (4) Gradient-weighted Class Activation Mapping (Grad-CAM) technique is employed to check the decision models’ transparency and understand their ability to arrive at a decision by visualizing the discriminative image regions and localizing the chest diseases. The proposed work is compared with existing disease prediction models on chest X-rays and state-of-the-art deep learning strategies to assess the effectiveness of the proposed model. The proposed model is tested with a publicly available Open-I Dataset and data collected from a private hospital. After the comprehensive assessment, it is observed that the performance of the designed approach showcased a 7% to 18% increase in accuracy compared to the existing method.
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Wang, Zhiqiang. "Two Postestimation Commands for Assessing Confounding Effects in Epidemiological Studies." Stata Journal: Promoting communications on statistics and Stata 7, no. 2 (June 2007): 183–96. http://dx.doi.org/10.1177/1536867x0700700203.
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Confounding is a major issue in observational epidemiological studies. This paper describes two postestimation commands for assessing confounding effects. One command (confall) displays and plots all possible effect estimates against one of p-value, Akaike information criterion, or Bayesian information criterion. This computing-intensive procedure allows researchers to inspect the variability of the effect estimates from various possible models. Another command (chest) uses a stepwise approach to identify variables that have substantially changed the effect estimate. Both commands can be used after most common estimation commands in epidemiological studies, such as logistic regression, conditional logistic regression, Poisson regression, linear regression, and Cox proportional hazards models.
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Ragab, Mahmoud, Samah Alshehri, Nabil A. Alhakamy, Romany F. Mansour, and Deepika Koundal. "Multiclass Classification of Chest X-Ray Images for the Prediction of COVID-19 Using Capsule Network." Computational Intelligence and Neuroscience 2022 (May 19, 2022): 1–8. http://dx.doi.org/10.1155/2022/6185013.
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It is critical to establish a reliable method for detecting people infected with COVID-19 since the pandemic has numerous harmful consequences worldwide. If the patient is infected with COVID-19, a chest X-ray can be used to determine this. In this work, an X-ray showing a COVID-19 infection is classified by the capsule neural network model we trained to recognise. 6310 chest X-ray pictures were used to train the models, separated into three categories: normal, pneumonia, and COVID-19. This work is considered an improved deep learning model for the classification of COVID-19 disease through X-ray images. Viewpoint invariance, fewer parameters, and better generalisation are some of the advantages of CapsNet compared with the classic convolutional neural network (CNN) models. The proposed model has achieved an accuracy greater than 95% during the model’s training, which is better than the other state-of-the-art algorithms. Furthermore, to aid in detecting COVID-19 in a chest X-ray, the model could provide extra information.
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Uddin, Azher, Bayazid Talukder, Mohammad Monirujjaman Khan, and Atef Zaguia. "Study on Convolutional Neural Network to Detect COVID-19 from Chest X-Rays." Mathematical Problems in Engineering 2021 (September 9, 2021): 1–11. http://dx.doi.org/10.1155/2021/3366057.
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The world is facing a pandemic due to the coronavirus disease 2019 (COVID-19), named as per the World Health Organization. COVID-19 is caused by the virus called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which was initially discovered in late December 2019 in Wuhan, China. Later, the virus had spread throughout the world within a few months. COVID-19 has become a global health crisis because millions of people worldwide are affected by this fatal virus. Fever, dry cough, and gastrointestinal problems are the most common signs of COVID-19. The disease is highly contagious, and affected people can easily spread the virus to those with whom they have close contact. Thus, contact tracing is a suitable solution to prevent the virus from spreading. The method of identifying all persons with whom a COVID-19-affected patient has come into contact in the last 2 weeks is called contact tracing. This study presents an investigation of a convolutional neural network (CNN), which makes the test faster and more reliable, to detect COVID-19 from chest X-ray (CXR) images. Because there are many studies in this field, the designed model focuses on increasing the accuracy level and uses a transfer learning approach and a custom model. Pretrained deep CNN models, such as VGG16, InceptionV3, MobileNetV2, and ResNet50, have been used for deep feature extraction. The performance measurement in this study was based on classification accuracy. The results of this study indicate that deep learning can recognize SARS-CoV-2 from CXR images. The designed model provided 93% accuracy and 98% validation accuracy, and the pretrained customized models such as MobileNetV2 obtained 97% accuracy, InceptionV3 obtained 98%, and VGG16 obtained 98% accuracy, respectively. Among these models, InceptionV3 has recorded the highest accuracy.
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Ahmad, Fareed, Amjad Farooq, and Muhammad Usman Ghani. "Deep Ensemble Model for Classification of Novel Coronavirus in Chest X-Ray Images." Computational Intelligence and Neuroscience 2021 (January 5, 2021): 1–17. http://dx.doi.org/10.1155/2021/8890226.
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The novel coronavirus, SARS-CoV-2, can be deadly to people, causing COVID-19. The ease of its propagation, coupled with its high capacity for illness and death in infected individuals, makes it a hazard to the community. Chest X-rays are one of the most common but most difficult to interpret radiographic examination for early diagnosis of coronavirus-related infections. They carry a considerable amount of anatomical and physiological information, but it is sometimes difficult even for the expert radiologist to derive the related information they contain. Automatic classification using deep learning models can help in better assessing these infections swiftly. Deep CNN models, namely, MobileNet, ResNet50, and InceptionV3, were applied with different variations, including training the model from the start, fine-tuning along with adjusting learned weights of all layers, and fine-tuning with learned weights along with augmentation. Fine-tuning with augmentation produced the best results in pretrained models. Out of these, two best-performing models (MobileNet and InceptionV3) selected for ensemble learning produced accuracy and FScore of 95.18% and 90.34%, and 95.75% and 91.47%, respectively. The proposed hybrid ensemble model generated with the merger of these deep models produced a classification accuracy and FScore of 96.49% and 92.97%. For test dataset, which was separately kept, the model generated accuracy and FScore of 94.19% and 88.64%. Automatic classification using deep ensemble learning can help radiologists in the correct identification of coronavirus-related infections in chest X-rays. Consequently, this swift and computer-aided diagnosis can help in saving precious human lives and minimizing the social and economic impact on society.
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Kenyon, C. M., T. J. Pedley, and T. W. Higenbottam. "Adaptive modeling of the human rib cage in median sternotomy." Journal of Applied Physiology 70, no. 5 (May 1, 1991): 2287–302. http://dx.doi.org/10.1152/jappl.1991.70.5.2287.
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This paper describes a limited computer-analyzed kinematic model of the rib cage that can be adapted to individual subjects. Also described is its validation and use in assessing the changes in chest wall shape after coronary artery bypass graft (CABG) surgery in 12 patients. The positions of a small number of anatomic locations on the thoracic spine, ribs, manubrium, and sternum are measured from lateral and posterior-anterior chest radiographs. The computer program puts these two views together removing the magnification and reconstructs any missing points to give a three-dimensional picture of the rib cage to which mathematical models of the bones are scaled. The patients had chest radiographs taken at total lung capacity (TLC) and residual volume (RV) to investigate the source of the restrictive ventilatory defect that follows CABG. The predictions from the model were tested by comparing full-sized computer plots with the actual chest radiographs. The estimates of the bony structures were accurate to +/- 3 degrees for orientations and +/- 6 mm for positions. We found reduced rib motion both "pump-handle" (theta) and "bucket handle" (psi) going from theta, psi left, psi right = 9 degrees, 10 degrees, 14 degrees to 4 degrees, 10 degrees, 9 degrees, respectively, after surgery with P less than 0.025, 0.42, 0.07. The angles were measured from the horizontal and increased caudally. There was also reduction in the range of angles subtended by the arc of the thoracic vertebrae between TLC and RV, which went from 12 degrees to -1 degrees (P less than 0.015). These data explain the fall in lung volumes that follow CABG and provide insight into the contribution made by the ribs and spine in full inspiration and full expiration.
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Khan, Muhammad Attique, Marium Azhar, Kainat Ibrar, Abdullah Alqahtani, Shtwai Alsubai, Adel Binbusayyis, Ye Jin Kim, and Byoungchol Chang. "COVID-19 Classification from Chest X-Ray Images: A Framework of Deep Explainable Artificial Intelligence." Computational Intelligence and Neuroscience 2022 (July 14, 2022): 1–14. http://dx.doi.org/10.1155/2022/4254631.
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COVID-19 detection and classification using chest X-ray images is a current hot research topic based on the important application known as medical image analysis. To halt the spread of COVID-19, it is critical to identify the infection as soon as possible. Due to time constraints and the expertise of radiologists, manually diagnosing this infection from chest X-ray images is a difficult and time-consuming process. Artificial intelligence techniques have had a significant impact on medical image analysis and have also introduced several techniques for COVID-19 diagnosis. Deep learning and explainable AI have shown significant popularity among AL techniques for COVID-19 detection and classification. In this work, we propose a deep learning and explainable AI technique for the diagnosis and classification of COVID-19 using chest X-ray images. Initially, a hybrid contrast enhancement technique is proposed and applied to the original images that are later utilized for the training of two modified deep learning models. The deep transfer learning concept is selected for the training of pretrained modified models that are later employed for feature extraction. Features of both deep models are fused using improved canonical correlation analysis that is further optimized using a hybrid algorithm named Whale-Elephant Herding. Through this algorithm, the best features are selected and classified using an extreme learning machine (ELM). Moreover, the modified deep models are utilized for Grad-CAM visualization. The experimental process was conducted on three publicly available datasets and achieved accuracies of 99.1, 98.2, and 96.7%, respectively. Moreover, the ablation study was performed and showed that the proposed accuracy is better than the other methods.
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Acharya, Vasundhara, Gaurav Dhiman, Krishna Prakasha, Pranshu Bahadur, Ankit Choraria, Sushobhitha M, Sowjanya J, et al. "AI-Assisted Tuberculosis Detection and Classification from Chest X-Rays Using a Deep Learning Normalization-Free Network Model." Computational Intelligence and Neuroscience 2022 (October 3, 2022): 1–19. http://dx.doi.org/10.1155/2022/2399428.
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Tuberculosis (TB) is an airborne disease caused by Mycobacterium tuberculosis. It is imperative to detect cases of TB as early as possible because if left untreated, there is a 70% chance of a patient dying within 10 years. The necessity for supplementary tools has increased in mid to low-income countries due to the rise of automation in healthcare sectors. The already limited resources are being heavily allocated towards controlling other dangerous diseases. Modern digital radiography (DR) machines, used for screening chest X-rays of potential TB victims are very practical. Coupled with computer-aided detection (CAD) with the aid of artificial intelligence, radiologists working in this field can really help potential patients. In this study, progressive resizing is introduced for training models to perform automatic inference of TB using chest X-ray images. ImageNet fine-tuned Normalization-Free Networks (NFNets) are trained for classification and the Score-Cam algorithm is utilized to highlight the regions in the chest X-Rays for detailed inference on the diagnosis. The proposed method is engineered to provide accurate diagnostics for both binary and multiclass classification. The models trained with this method have achieved 96.91% accuracy, 99.38% AUC, 91.81% sensitivity, and 98.42% specificity on a multiclass classification dataset. Moreover, models have also achieved top-1 inference metrics of 96% accuracy and 98% AUC for binary classification. The results obtained demonstrate that the proposed method can be used as a secondary decision tool in a clinical setting for assisting radiologists.
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Prasanna, S. L., and Nagendra Panini Challa. "Heart Disease Prediction Using Optimal Mayfly Technique with Ensemble Models." International Journal of Swarm Intelligence Research 13, no. 1 (January 1, 2022): 1–22. http://dx.doi.org/10.4018/ijsir.313665.
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This paper proposes a methodology consisting of two phases: attributes selection and classification based on the attributes selected. Phase 1 uses the introduced new feature selection algorithm which is the optimal mayfly algorithm (OMA) to solve the feature selection technique problem. Mayfly algorithm has derived features of physiological and anatomical relevance, like ST depression, the highest heart rate, cholesterol, chest pain, and heart vessels. In the second phase, the selected attributes use the ensemble classifiers like random subspace, bagging, and boosting. Optimal mayfly algorithm (OMA) with boosting technique had the highest accuracy. Therefore, true disease, false disease, accuracy, and specificity are measured to evaluate the proposed system's efficiency. It has been discovered that the proposed method, which combines feature selection and ensemble techniques performs well, the performance of the optimal mayfly algorithm along with ensemble classifiers of boosting method with a model accuracy of 97.12% which is the highest accuracy value compared to any single model.
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Krishnamurthy, Surya, Kathiravan Srinivasan, Saeed Mian Qaisar, P. M. Durai Raj Vincent, and Chuan-Yu Chang. "Evaluating Deep Neural Network Architectures with Transfer Learning for Pneumonitis Diagnosis." Computational and Mathematical Methods in Medicine 2021 (September 12, 2021): 1–12. http://dx.doi.org/10.1155/2021/8036304.
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Pneumonitis is an infectious disease that causes the inflammation of the air sac. It can be life-threatening to the very young and elderly. Detection of pneumonitis from X-ray images is a significant challenge. Early detection and assistance with diagnosis can be crucial. Recent developments in the field of deep learning have significantly improved their performance in medical image analysis. The superior predictive performance of the deep learning methods makes them ideal for pneumonitis classification from chest X-ray images. However, training deep learning models can be cumbersome and resource-intensive. Reusing knowledge representations of public models trained on large-scale datasets through transfer learning can help alleviate these challenges. In this paper, we compare various image classification models based on transfer learning with well-known deep learning architectures. The Kaggle chest X-ray dataset was used to evaluate and compare our models. We apply basic data augmentation and fine-tune our feed-forward classification head on the models pretrained on the ImageNet dataset. We observed that the DenseNet201 model outperforms other models with an AUROC score of 0.966 and a recall score of 0.99. We also visualize the class activation maps from the DenseNet201 model to interpret the patterns recognized by the model for prediction.
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Jiang, Qiongqin, Wenguang Song, Gaoming Yu, Ming Zhao, Bowen Li, Haoyuan Li, and Qian Yu. "Research on Intelligent Recognition Algorithm of Pneumonia Based on Deep Convolution and Attention Neural Network." Mathematical Problems in Engineering 2021 (September 11, 2021): 1–13. http://dx.doi.org/10.1155/2021/1927860.
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Pneumonia is a common infection that inflames the air sacs in the lungs, causing symptoms such as difficulty breathing and fever. Although pneumonia is not difficult to treat, prompt diagnosis is crucial. Without proper treatment, pneumonia can be fatal, especially in children and the elderly. Chest x-rays are an affordable way to diagnose pneumonia. Investigating an algorithmic model that can reliably and intelligently classify pneumonia based on chest X-ray images could greatly reduce the burden on physicians. The advantages and disadvantages of each of the four convolutional neural networks VGG16, ResNet50, DenseNet201, and DWA algorithm models are analyzed and given by comparing and investigating each model. The VGG16, ResNet50, and DenseNet201 network models are compared with the DWA model. When training the depthwise separable convolution with attention neural network (DWA), the training accuracy reaches 97.5%. The validation accuracy was 79% due to the model’s tendency to overfit, and the test dataset had 1175 X-ray images with a test accuracy of 96.1%. The experimental results illustrate the effectiveness of the attention mechanism and the reliability of the deeply separable convolutional neural network algorithm. The successful application of the deep learning algorithm proposed in this paper on pneumonia recognition will provide an objective, accurate, and fast solution for medical practitioners and can provide a fast and accurate pneumonia diagnosis system for doctors.
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Hemantkumar, Purohit Om, Rakshit Lodha, Meghna Bajoria, and R. Sujatha. "Pneumonia Detection Using Deep Learning Architectures." Journal of Computational and Theoretical Nanoscience 17, no. 12 (December 1, 2020): 5535–42. http://dx.doi.org/10.1166/jctn.2020.9450.
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Pneumonia is an infection caused by bacteria and viruses. It can shift from mellow to serious cases. This disease causes severe damages to the lungs since they fill with fluids. This situation causes difficulty in breathing. It further prevents oxygen to reach the blood. Pneumonia is diagnosed with the help of a chest X-rays, which can also use in the diagnosis of diseases like emphysema, lung cancer, and tuberculosis. According to WHO (World Health Organization (WHO). 2001. Standardization of Interpretation of Chest Radiographs for the Diagnosis of Pneumonia in Children. p.4.), Chest X-rays, at present, is the best available method for detecting pneumonia. Feature extraction methods like DiscreteWavelet Transform (DWT),Wavelet Frame Transform (WFT), andWavelet Packet Transform (WPT) can be used followed by any classification algorithm. In this paper, models like Squeezenet, DenseNet, and Resnet34 have been used for image recognition. In our system, the medical images were taken from Kaggle database and were recorded using a suitable imaging system. The images retrieved were then considered for input for the system where the images go through the various phases of image processing like pre-processing, edge detection and feature extraction. Later, a variety of training models are applied to know which model offers the highest accuracy.
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Abiyev, Rahib H., and Abdullahi Ismail. "COVID-19 and Pneumonia Diagnosis in X-Ray Images Using Convolutional Neural Networks." Mathematical Problems in Engineering 2021 (November 24, 2021): 1–14. http://dx.doi.org/10.1155/2021/3281135.
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This paper proposes a Convolutional Neural Networks (CNN) based model for the diagnosis of COVID-19 and non-COVID-19 viral pneumonia diseases. These diseases affect and damage the human lungs. Early diagnosis of patients infected by the virus can help save the patient’s life and prevent the further spread of the virus. The CNN model is used to help in the early diagnosis of the virus using chest X-ray images, as it is one of the fastest and most cost-effective ways of diagnosing the disease. We proposed two convolutional neural networks (CNN) models, which were trained using two different datasets. The first model was trained for binary classification with one of the datasets that only included pneumonia cases and normal chest X-ray images. The second model made use of the knowledge learned by the first model using transfer learning and trained for 3 class classifications on COVID-19, pneumonia, and normal cases based on the second dataset that included chest X-ray (CXR) images. The effect of transfer learning on model constriction has been demonstrated. The model gave promising results in terms of accuracy, recall, precision, and F1_score with values of 98.3%, 97.9%, 98.3%, and 98.0%, respectively, on the test data. The proposed model can diagnose the presence of COVID-19 in CXR images; hence, it will help radiologists make diagnoses easily and more accurately.
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Kapadia, Mayank R., and Chirag N. Paunwala. "Content Based Medical Image Retrieval for Accurate Disease Diagnosis." Open Biomedical Engineering Journal 15, no. 1 (December 31, 2021): 236–49. http://dx.doi.org/10.2174/1874120702115010236.
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Introduction: Content Based Image Retrieval (CBIR) system is an innovative technology to retrieve images from various media types. One of the CBIR applications is Content Based Medical Image Retrieval (CBMIR). The image retrieval system retrieves the most similar images from the historical cases, and such systems can only support the physician's decision to diagnose a disease. To extract the useful features from the query image for linking similar types of images is the major challenge in the CBIR domain. The Convolution Neural Network (CNN) can overcome the drawbacks of traditional algorithms, dependent on the low-level feature extraction technique. Objective: The objective of the study is to develop a CNN model with a minimum number of convolution layers and to get the maximum possible accuracy for the CBMIR system. The minimum number of convolution layers reduces the number of mathematical operations and the time for the model's training. It also reduces the number of training parameters, like weights and bias. Thus, it reduces the memory requirement for the model storage. This work mainly focused on developing an optimized CNN model for the CBMIR system. Such systems can only support the physicians' decision to diagnose a disease from the images and retrieve the relevant cases to help the doctor decide the precise treatment. Methods: The deep learning-based model is proposed in this paper. The experiment is done with several convolution layers and various optimizers to get the maximum accuracy with a minimum number of convolution layers. Thus, the ten-layer CNN model is developed from scratch and used to derive the training and testing images' features and classify the test image. Once the image class is identified, the most relevant images are determined based on the Euclidean distance between the query features and database features of the identified class. Based on this distance, the most relevant images are displayed from the respective class of images. The general dataset CIFAR10, which has 60,000 images of 10 different classes, and the medical dataset IRMA, which has 2508 images of 9 various classes, have been used to analyze the proposed method. The proposed model is also applied for the medical x-ray image dataset of chest disease and compared with the other pre-trained models. Results: The accuracy and the average precision rate are the measurement parameters utilized to compare the proposed model with different machine learning techniques. The accuracy of the proposed model for the CIFAR10 dataset is 93.9%, which is better than the state-of-the-art methods. After the success for the general dataset, the model is also tested for the medical dataset. For the x-ray images of the IRMA dataset, it is 86.53%, which is better than the different pre-trained model results. The model is also tested for the other x-ray dataset, which is utilized to identify chest-related disease. The average precision rate for such a dataset is 97.25%. Also, the proposed model fulfills the major challenge of the semantic gap. The semantic gap of the proposed model for the chest disease dataset is 2.75%, and for the IRMA dataset, it is 13.47%. Also, only ten convolution layers are utilized in the proposed model, which is very small in number compared to the other pre-trained models. Conclusion: The proposed technique shows remarkable improvement in performance metrics over CNN-based state-of-the-art methods. It also offers a significant improvement in performance metrics over different pre-trained models for the two different medical x-ray image datasets.
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Kapadia, Mayank R., and Chirag N. Paunwala. "Content Based Medical Image Retrieval for Accurate Disease Diagnosis." Open Biomedical Engineering Journal 15, no. 1 (December 31, 2021): 235–48. http://dx.doi.org/10.2174/1874120702115010235.
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Introduction: Content Based Image Retrieval (CBIR) system is an innovative technology to retrieve images from various media types. One of the CBIR applications is Content Based Medical Image Retrieval (CBMIR). The image retrieval system retrieves the most similar images from the historical cases, and such systems can only support the physician's decision to diagnose a disease. To extract the useful features from the query image for linking similar types of images is the major challenge in the CBIR domain. The Convolution Neural Network (CNN) can overcome the drawbacks of traditional algorithms, dependent on the low-level feature extraction technique. Objective: The objective of the study is to develop a CNN model with a minimum number of convolution layers and to get the maximum possible accuracy for the CBMIR system. The minimum number of convolution layers reduces the number of mathematical operations and the time for the model's training. It also reduces the number of training parameters, like weights and bias. Thus, it reduces the memory requirement for the model storage. This work mainly focused on developing an optimized CNN model for the CBMIR system. Such systems can only support the physicians' decision to diagnose a disease from the images and retrieve the relevant cases to help the doctor decide the precise treatment. Methods: The deep learning-based model is proposed in this paper. The experiment is done with several convolution layers and various optimizers to get the maximum accuracy with a minimum number of convolution layers. Thus, the ten-layer CNN model is developed from scratch and used to derive the training and testing images' features and classify the test image. Once the image class is identified, the most relevant images are determined based on the Euclidean distance between the query features and database features of the identified class. Based on this distance, the most relevant images are displayed from the respective class of images. The general dataset CIFAR10, which has 60,000 images of 10 different classes, and the medical dataset IRMA, which has 2508 images of 9 various classes, have been used to analyze the proposed method. The proposed model is also applied for the medical x-ray image dataset of chest disease and compared with the other pre-trained models. Results: The accuracy and the average precision rate are the measurement parameters utilized to compare the proposed model with different machine learning techniques. The accuracy of the proposed model for the CIFAR10 dataset is 93.9%, which is better than the state-of-the-art methods. After the success for the general dataset, the model is also tested for the medical dataset. For the x-ray images of the IRMA dataset, it is 86.53%, which is better than the different pre-trained model results. The model is also tested for the other x-ray dataset, which is utilized to identify chest-related disease. The average precision rate for such a dataset is 97.25%. Also, the proposed model fulfills the major challenge of the semantic gap. The semantic gap of the proposed model for the chest disease dataset is 2.75%, and for the IRMA dataset, it is 13.47%. Also, only ten convolution layers are utilized in the proposed model, which is very small in number compared to the other pre-trained models. Conclusion: The proposed technique shows remarkable improvement in performance metrics over CNN-based state-of-the-art methods. It also offers a significant improvement in performance metrics over different pre-trained models for the two different medical x-ray image datasets.
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Md Ali, Mohd Adli, Mohd Radhwan Abidin, Nik Arsyad Nik Muhamad Affendi, Hafidzul Abdullah, Daaniyal R. Rosman, Nu'man Barud'din, Faiz Kemi, and Farid Hayati. "CLASSIFICATION OF CHEST RADIOGRAPHS USING NOVEL ANOMALOUS SALIENCY MAP AND DEEP CONVOLUTIONAL NEURAL NETWORK." IIUM Engineering Journal 22, no. 2 (July 4, 2021): 234–48. http://dx.doi.org/10.31436/iiumej.v22i2.1752.
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The rapid advancement in pattern recognition via the deep learning method has made it possible to develop an autonomous medical image classification system. This system has proven robust and accurate in classifying most pathological features found in a medical image, such as airspace opacity, mass, and broken bone. Conventionally, this system takes routine medical images with minimum pre-processing as the model's input; in this research, we investigate if saliency maps can be an alternative model input. Recent research has shown that saliency maps' application increases deep learning model performance in image classification, object localization, and segmentation. However, conventional bottom-up saliency map algorithms regularly failed to localize salient or pathological anomalies in medical images. This failure is because most medical images are homogenous, lacking color, and contrast variant. Therefore, we also introduce the Xenafas algorithm in this paper. The algorithm creates a new kind of anomalous saliency map called the Intensity Probability Mapping and Weighted Intensity Probability Mapping. We tested the proposed saliency maps on five deep learning models based on common convolutional neural network architecture. The result of this experiment showed that using the proposed saliency map over regular radiograph chest images increases the sensitivity of most models in identifying images with air space opacities. Using the Grad-CAM algorithm, we showed how the proposed saliency map shifted the model attention to the relevant region in chest radiograph images. While in the qualitative study, it was found that the proposed saliency map regularly highlights anomalous features, including foreign objects and cardiomegaly. However, it is inconsistent in highlighting masses and nodules. ABSTRAK: Perkembangan pesat sistem pengecaman corak menggunakan kaedah pembelajaran mendalam membolehkan penghasilan sistem klasifikasi gambar perubatan secara automatik. Sistem ini berupaya menilai secara tepat jika terdapat tanda-tanda patologi di dalam gambar perubatan seperti kelegapan ruang udara, jisim dan tulang patah. Kebiasaannya, sistem ini akan mengambil gambar perubatan dengan pra-pemprosesan minimum sebagai input. Kajian ini adalah tentang potensi peta salien dapat dijadikan sebagai model input alternatif. Ini kerana kajian terkini telah menunjukkan penggunaan peta salien dapat meningkatkan prestasi model pembelajaran mendalam dalam pengklasifikasian gambar, pengesanan objek, dan segmentasi gambar. Walau bagaimanapun, sistem konvensional algoritma peta salien jenis bawah-ke-atas kebiasaannya gagal mengesan salien atau anomali patologi dalam gambar-gambar perubatan. Kegagalan ini disebabkan oleh sifat gambar perubatan yang homogen, kurang variasi warna dan kontras. Oleh itu, kajian ini memperkenalkan algoritma Xenafas yang menghasilkan dua jenis pemetaan saliensi anomali iaitu Pemetaan Kebarangkalian Keamatan dan Pemetaan Kebarangkalian Keamatan Pemberat. Kajian dibuat pada peta salien yang dicadangkan iaitu pada lima model pembelajaran mendalam berdasarkan seni bina rangkaian neural konvolusi yang sama. Dapatan kajian menunjukkan dengan menggunakan peta salien atas gambar-gambar radiografi dada tetap membantu kesensitifan kebanyakan model dalam mengidentifikasi gambar-gambar dengan kelegapan ruang udara. Dengan menggunakan algoritma Grad-CAM, peta salien yang dicadangkan ini mampu mengalih fokus model kepada kawasan yang relevan kepada gambar radiografi dada. Sementara itu, kajian kualitatif ini juga menunjukkan algoritma yang dicadangkan mampu memberi ciri anomali, termasuk objek asing dan kardiomegali. Walau bagaimanapun, ianya tidak konsisten dalam menjelaskan berat dan nodul.
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Ahsan, Md Manjurul, Tasfiq E. Alam, Theodore Trafalis, and Pedro Huebner. "Deep MLP-CNN Model Using Mixed-Data to Distinguish between COVID-19 and Non-COVID-19 Patients." Symmetry 12, no. 9 (September 16, 2020): 1526. http://dx.doi.org/10.3390/sym12091526.
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The limitations and high false-negative rates (30%) of COVID-19 test kits have been a prominent challenge during the 2020 coronavirus pandemic. Manufacturing those kits and performing the tests require extensive resources and time. Recent studies show that radiological images like chest X-rays can offer a more efficient solution and faster initial screening of COVID-19 patients. In this study, we develop a COVID-19 diagnosis model using Multilayer Perceptron and Convolutional Neural Network (MLP-CNN) for mixed-data (numerical/categorical and image data). The model predicts and differentiates between COVID-19 and non-COVID-19 patients, such that early diagnosis of the virus can be initiated, leading to timely isolation and treatments to stop further spread of the disease. We also explore the benefits of using numerical/categorical data in association with chest X-ray images for screening COVID-19 patients considering both balanced and imbalanced datasets. Three different optimization algorithms are used and tested:adaptive learning rate optimization algorithm (Adam), stochastic gradient descent (Sgd), and root mean square propagation (Rmsprop). Preliminary computational results show that, on a balanced dataset, a model trained with Adam can distinguish between COVID-19 and non-COVID-19 patients with a higher accuracy of 96.3%. On the imbalanced dataset, the model trained with Rmsprop outperformed all other models by achieving an accuracy of 95.38%. Additionally, our proposed model outperformed selected existing deep learning models (considering only chest X-ray or CT scan images) by producing an overall average accuracy of 94.6% ± 3.42%.
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Takizawa, Hotaka, Shinji Yamamoto, Tohru Nakagawa, Tohru Matsumoto, Yukio Tateno, Takeshi Iinuma, and Mitsuomi Matsumoto. "Recognition of lung nodule shadows from chest X-ray CT images using 3D Markov random field models." Systems and Computers in Japan 35, no. 8 (2004): 82–95. http://dx.doi.org/10.1002/scj.10453.
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Hatchuel, Armand, Pascal Le Masson, Benoit Weil, and Daniel Carvajal-Perez. "Innovative Design Within Tradition - Injecting Topos Structures in C-K Theory to Model Culinary Creation Heritage." Proceedings of the Design Society: International Conference on Engineering Design 1, no. 1 (July 2019): 1543–52. http://dx.doi.org/10.1017/dsi.2019.160.
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AbstractIn “Grande cuisine” creation and tradition co-evolve in a rich number of ways. Great chefs still use recipes from the 19th century and may also reinvent gastronomy itself. The creation heritage of culinary Art is the paradoxical capacity to both “respect” tradition and “break” its rules. Building on C-K theory, we show that such creative heritage needs multiple and independent layers of knowledge that “speak” of basic fixed objects. These properties correspond to general mathematical structures that we find in Topos theory. Thus, C-K/Topos predicts creative design strategies that can respect tradition in different ways. It also proves a form of “innovation within tradition” - “sheafification” in Topos words- that is not a compromise and builds on tradition itself. These findings fit with the lessons of great books of gastronomy. C-K/Topos has a wide scope of validity: it applies to any innovative design that needs preserving systemic structures, like engineering systems or social and environmental systems. C- K/Topos models with a high generality how local and radical innovation can warrant systems incremental change. C-K/Topos will have implications for teaching and research.
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Helwan, Abdulkader, Mohammad Khaleel Sallam Ma’aitah, Hani Hamdan, Dilber Uzun Ozsahin, and Ozum Tuncyurek. "Radiologists versus Deep Convolutional Neural Networks: A Comparative Study for Diagnosing COVID-19." Computational and Mathematical Methods in Medicine 2021 (May 10, 2021): 1–9. http://dx.doi.org/10.1155/2021/5527271.
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The reverse transcriptase polymerase chain reaction (RT-PCR) is still the routinely used test for the diagnosis of SARS-CoV-2 (COVID-19). However, according to several reports, RT-PCR showed a low sensitivity and multiple tests may be required to rule out false negative results. Recently, chest computed tomography (CT) has been an efficient tool to diagnose COVID-19 as it is directly affecting the lungs. In this paper, we investigate the application of pre-trained models in diagnosing patients who are positive for COVID-19 and differentiating it from normal patients, who tested negative for coronavirus. The study aims to compare the generalization capabilities of deep learning models with two thoracic radiologists in diagnosing COVID-19 chest CT images. A dataset of 3000 images was obtained from the Near East Hospital, Cyprus, and used to train and to test the three employed pre-trained models. In a test set of 250 images used to evaluate the deep neural networks and the radiologists, it was found that deep networks (ResNet-18, ResNet-50, and DenseNet-201) can outperform the radiologists in terms of higher accuracy (97.8%), sensitivity (98.1%), specificity (97.3%), precision (98.4%), and F1-score (198.25%), in classifying COVID-19 images.
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Butler, J. P., J. Huang, S. H. Loring, S. J. Lai-Fook, P. M. Wang, and T. A. Wilson. "Model for a pump that drives circulation of pleural fluid." Journal of Applied Physiology 78, no. 1 (January 1, 1995): 23–29. http://dx.doi.org/10.1152/jappl.1995.78.1.23.
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Physical and mathematical models were used to study a mechanism that could maintain the layer of pleural fluid that covers the surface of the lung. The pleural space was modeled as a thin layer of viscous fluid lying between a membrane carrying tension (T), representing the lung, and a rigid wall, representing the chest wall. Flow of the fluid was driven by sliding between the membrane and wall. The physical model consisted of a cylindrical balloon with strings stretched along its surface. When the balloon was inflated inside a vertical circular cylinder containing a viscous fluid, the strings formed narrow vertical channels between broad regions in which the balloon pressed against the outer cylinder. The channels simulated the pleural space in the regions of lobar margins. Oscillatory rotation of the outer cylinder maintained a lubricating layer of fluid between the balloon and the cylinder. The thickness of the fluid layer (h), measured by fluorescence videomicroscopy, was larger for larger fluid viscosity (mu), larger sliding velocity (U), and smaller pressure difference (delta P) between the layer and the channel. A mathematical model of the flow in a horizontal section was analyzed, and numerical solutions were obtained for parameter values of mu, U, delta P, and T that matched those of the physical model. The computed results agreed reasonably well with the experimental results. Scaling laws yield the prediction that h is approximately (T/delta P)(microU/T)2/3. For physiological values of the parameters, the predicted value of h is approximately 10(-3) cm, in good agreement with the observed thickness of the pleural space.
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Archana, K. S., B. Sivakumar, Ramya Kuppusamy, Yuvaraja Teekaraman, and Arun Radhakrishnan. "Automated Cardioailment Identification and Prevention by Hybrid Machine Learning Models." Computational and Mathematical Methods in Medicine 2022 (February 15, 2022): 1–8. http://dx.doi.org/10.1155/2022/9797844.
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Accurate prediction of cardiovascular disease is necessary and considered to be a difficult attempt to treat a patient effectively before a heart attack occurs. According to recent studies, heart disease is said to be one of the leading origins of death worldwide. Early identification of CHD can assist to reduce death rates. When it comes to prediction using traditional methodologies, the difficulty arises in the intricacy of the data and relationships. This research is aimed at applying recent machine learning technology to identify heart disease from past medical data to uncover correlations in data that can greatly improve the accuracy of prediction rates using various machine learning models. Models have been implemented using naive Bayes, random forest algorithms, and the combinations of two models such as naive Bayes and random forest methods. These methods offer numerous attributes associated with heart disease. This proposed system foresees the chance of rising heart disease. The proposed system uses 14 parameters such as age, sex, quick blood sugar, chest discomfort, and other medical parameters which are used in the proposed system. Our proposed systems find the probability of developing heart disease in percentages as well as the accuracy level (accuracy of 93%). Finally, this proposed method will support the doctors to analyze the heart patients competently.
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Qaid, Talal S., Hussein Mazaar, Mohammad Yahya H. Al-Shamri, Mohammed S. Alqahtani, Abeer A. Raweh, and Wafaa Alakwaa. "Hybrid Deep-Learning and Machine-Learning Models for Predicting COVID-19." Computational Intelligence and Neuroscience 2021 (August 3, 2021): 1–11. http://dx.doi.org/10.1155/2021/9996737.
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The COVID-19 pandemic has had a significant impact on public life and health worldwide, putting the world’s healthcare systems at risk. The first step in stopping this outbreak is to detect the infection in its early stages, which will relieve the risk, control the outbreak’s spread, and restore full functionality to the world’s healthcare systems. Currently, PCR is the most prevalent diagnosis tool for COVID-19. However, chest X-ray images may play an essential role in detecting this disease, as they are successful for many other viral pneumonia diseases. Unfortunately, there are common features between COVID-19 and other viral pneumonia, and hence manual differentiation between them seems to be a critical problem and needs the aid of artificial intelligence. This research employs deep- and transfer-learning techniques to develop accurate, general, and robust models for detecting COVID-19. The developed models utilize either convolutional neural networks or transfer-learning models or hybridize them with powerful machine-learning techniques to exploit their full potential. For experimentation, we applied the proposed models to two data sets: the COVID-19 Radiography Database from Kaggle and a local data set from Asir Hospital, Abha, Saudi Arabia. The proposed models achieved promising results in detecting COVID-19 cases and discriminating them from normal and other viral pneumonia with excellent accuracy. The hybrid models extracted features from the flatten layer or the first hidden layer of the neural network and then fed these features into a classification algorithm. This approach enhanced the results further to full accuracy for binary COVID-19 classification and 97.8% for multiclass classification.
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Bondarenko, Ol'ga. "ASSESSMENT OF THE SAFETY OF PASSENGER CARS IN CASE OF AN EMERGENCY ROLLOVER ON THE RAILROAD TRACKS." Bulletin of Bryansk state technical university 2021, no. 9 (September 8, 2021): 49–54. http://dx.doi.org/10.30987/1999-8775-2021-9-49-54.
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The purpose of the work is to assess the safety of passenger cars in case of an emergency rollover on the body of railroad tracks. The paper introduces a method for predicting injury of railway transport passengers as a result of swinging over the wagon on the body of railroad tracks. The method of research is mathematical modeling of scenarios of swinging over the wagon on a flat bottom or earth tramp of the railway track. A model of a passenger compartment has been developed, which is supplemented with models of a roomette, hand luggage and an anthropometric dummy. The originality of the work is the use of mannequin models for an accident with the rollover of a compartment car on the body of the railroad tracks and obtaining data on the interaction of fit models and a compartment car. The result of the study is the reported values of possible injury to passengers during an emergency rollover of a passenger car. Namely, the values of the head injury criterion, cervical vertebrae, breast and hips of the crash test dummy have been obtained. In comparison of the two considered scenarios of swinging over the wagon, the value of the head injury criterion for overturning the car on an inclined surface is 15% higher, the neck injury criterion is 30% higher, and the hip and chest injury criterion is 23% higher for mannequins on the upper shelves of the compartment due to their interaction with hand luggage. The obtained values do not exceed critical ones. The most dangerous positions of the mannequin model in the compartment of the car are revealed. Conclusions concerning the sufficient safety of the passenger car are formed and recommendations for the development of additional technical solutions to improve the safety of passenger cars are given.
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Sahlol, Ahmed T., Mohamed Abd Elaziz, Amani Tariq Jamal, Robertas Damaševičius, and Osama Farouk Hassan. "A Novel Method for Detection of Tuberculosis in Chest Radiographs Using Artificial Ecosystem-Based Optimisation of Deep Neural Network Features." Symmetry 12, no. 7 (July 8, 2020): 1146. http://dx.doi.org/10.3390/sym12071146.
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Tuberculosis (TB) is is an infectious disease that generally attacks the lungs and causes death for millions of people annually. Chest radiography and deep-learning-based image segmentation techniques can be utilized for TB diagnostics. Convolutional Neural Networks (CNNs) has shown advantages in medical image recognition applications as powerful models to extract informative features from images. Here, we present a novel hybrid method for efficient classification of chest X-ray images. First, the features are extracted from chest X-ray images using MobileNet, a CNN model, which was previously trained on the ImageNet dataset. Then, to determine which of these features are the most relevant, we apply the Artificial Ecosystem-based Optimization (AEO) algorithm as a feature selector. The proposed method is applied to two public benchmark datasets (Shenzhen and Dataset 2) and allows them to achieve high performance and reduced computational time. It selected successfully only the best 25 and 19 (for Shenzhen and Dataset 2, respectively) features out of about 50,000 features extracted with MobileNet, while improving the classification accuracy (90.2% for Shenzen dataset and 94.1% for Dataset 2). The proposed approach outperforms other deep learning methods, while the results are the best compared to other recently published works on both datasets.
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Singh, Akansha, Krishna Kant Singh, Michal Greguš, and Ivan Izonin. "CNGOD-An improved convolution neural network with grasshopper optimization for detection of COVID-19." Mathematical Biosciences and Engineering 19, no. 12 (2022): 12448–71. http://dx.doi.org/10.3934/mbe.2022584.
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<abstract><p>The world is facing the pandemic situation due to a beta corona virus named Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The disease caused by this virus known as Corona Virus Disease 2019 (COVID-19) has affected the entire world. The current diagnosis methods are laboratory based and require specialized testing kits for performing the test. Therefore, to overcome the limitations of testing kits a diagnosis method from chest X-ray images is proposed in this paper. Chest X-ray images can be easily obtained by X-ray machines that are readily available at medical centres. The radiological examinations augmented with chest X-ray images is an effective way of disease diagnosis. The automated analysis of the chest X-ray images requires a highly efficient method for identifying COVID-19 from these images. Thus, a novel deep convolution neural network (CNN) optimized using Grasshopper Optimization Algorithm (GOA) is proposed. The deep learning model comprises depth wise separable convolutions that independently look at cross channel and spatial correlations. The optimization of deep learning models is a complex task due the multiple layers and their non-linearities. In image classification problems optimizers like Adam, SGD etc. get stuck in local minima. Thus, in this paper a metaheuristic optimization algorithm is used to optimize the network. Grasshoper Optimization Algorithm (GOA) is a metaheuristic algorithm that mimics the behaviour of grasshoppers for food search. This algorithm is a fast converging and is capable of exploration and exploitation of large search spaces. Maximum Probability Based Cross Entropy Loss (MPCE) loss function is used as it minimizes the back propogation error of cross entropy and improves the training. The experimental results show that the proposed method gives high classification accuracy. The interpretation of results is augmented with class activation maps. Grad-CAM visualization algorithm is used for class activation maps.</p></abstract>
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Khan, Rijah, and Tahir Mehmood. "Classification of Thoracic Diseases Based on Chest X-ray Images Using Kernel Support Vector Machine." Mathematical Problems in Engineering 2022 (November 14, 2022): 1–9. http://dx.doi.org/10.1155/2022/9457730.
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Machine learning is the leading field of artificial intelligence that has achieved expert-level performance. Diagnosis and treatment of various medical diseases have led to advancements in medical imaging. Chest X-ray-based thoracic disease classification or identification is one of the potential applications in medical imaging based on machine learning. The study consists of 112,120 images of 30,804 individual patients with fourteen thoracic disease labels, which encapsulated the support vector machine (SVM). We have considered 04 kernels in SVM, namely, linear (L-SVM), polynomial (P-SVM), radial basis (R-SVM), and hyperbolic tangent (H-SVM) for classification of thoracic diseases based on X-ray images. To reduce the dimensionality and outliers from the SVM, variants are coupled with novel fast principal component analysis (FPCA). It appears that there is a significant p ≤ 0.05 difference between SVM variants where P-SVM and R-SVM next in order outperforms on most of the disease identification models with average validated classification accuracy ranging from 92% to 98%. The average calibrated accuracy ranges from 99.5% and reaches to 100% in most of the cases. The study is worth investigating as it is good for radiologists as they will be able to classify the diseases and it will help in improving and enhancing different medical techniques.
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Afifi, Ahmed, Noor E. Hafsa, Mona A. S. Ali, Abdulaziz Alhumam, and Safa Alsalman. "An Ensemble of Global and Local-Attention Based Convolutional Neural Networks for COVID-19 Diagnosis on Chest X-ray Images." Symmetry 13, no. 1 (January 11, 2021): 113. http://dx.doi.org/10.3390/sym13010113.
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The recent Coronavirus Disease 2019 (COVID-19) pandemic has put a tremendous burden on global health systems. Medical practitioners are under great pressure for reliable screening of suspected cases employing adjunct diagnostic tools to standard point-of-care testing methodology. Chest X-rays (CXRs) are appearing as a prospective diagnostic tool with easy-to-acquire, low-cost and less cross-contamination risk features. Artificial intelligence (AI)-attributed CXR evaluation has shown great potential for distinguishing COVID-19-induced pneumonia from other associated clinical instances. However, one of the associated challenges with diagnostic imaging-based modeling is incorrect feature attribution, which leads the model to learn misguiding disease patterns, causing wrong predictions. Here, we demonstrate an effective deep learning-based methodology to mitigate the problem, thereby allowing the classification algorithm to learn from relevant features. The proposed deep-learning framework consists of an ensemble of convolutional neural network (CNN) models focusing on both global and local pathological features from CXR lung images, while the latter is extracted using a multi-instance learning scheme and a local attention mechanism. An inspection of a series of backbone CNN models using global and local features, and an ensemble of both features, trained from high-quality CXR images of 1311 patients, further augmented for achieving the symmetry in class distribution, to localize lung pathological features followed by the classification of COVID-19 and other related pneumonia, shows that a DenseNet161 architecture outperforms all other models, as evaluated on an independent test set of 159 patients with confirmed cases. Specifically, an ensemble of DenseNet161 models with global and local attention-based features achieve an average balanced accuracy of 91.2%, average precision of 92.4%, and F1-score of 91.9% in a multi-label classification framework comprising COVID-19, pneumonia, and control classes. The DenseNet161 ensembles were also found to be statistically significant from all other models in a comprehensive statistical analysis. The current study demonstrated that the proposed deep learning-based algorithm can accurately identify the COVID-19-related pneumonia in CXR images, along with differentiating non-COVID-19-associated pneumonia with high specificity, by effectively alleviating the incorrect feature attribution problem, and exploiting an enhanced feature descriptor.
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Gocheva-Ilieva, Snezhana, Antoaneta Yordanova, and Hristina Kulina. "Predicting the 305-Day Milk Yield of Holstein-Friesian Cows Depending on the Conformation Traits and Farm Using Simplified Selective Ensembles." Mathematics 10, no. 8 (April 11, 2022): 1254. http://dx.doi.org/10.3390/math10081254.
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In animal husbandry, it is of great interest to determine and control the key factors that affect the production characteristics of animals, such as milk yield. In this study, simplified selective tree-based ensembles were used for modeling and forecasting the 305-day average milk yield of Holstein-Friesian cows, depending on 12 external traits and the farm as an environmental factor. The preprocessing of the initial independent variables included their transformation into rotated principal components. The resulting dataset was divided into learning (75%) and holdout test (25%) subsamples. Initially, three diverse base models were generated using Classifiction and Regression Trees (CART) ensembles and bagging and arcing algorithms. These models were processed using the developed simplified selective algorithm based on the index of agreement. An average reduction of 30% in the number of trees of selective ensembles was obtained. Finally, by separately stacking the predictions from the non-selective and selective base models, two linear hybrid models were built. The hybrid model of the selective ensembles showed a 13.6% reduction in the test set prediction error compared to the hybrid model of the non-selective ensembles. The identified key factors determining milk yield include the farm, udder width, chest width, and stature of the animals. The proposed approach can be applied to improve the management of dairy farms.
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Ma, Jianfeng, Jingyun Chen, Mailin Gan, Lei Chen, Ye Zhao, Yan Zhu, Lili Niu, Shunhua Zhang, Li Zhu, and Linyuan Shen. "Gut Microbiota Composition and Diversity in Different Commercial Swine Breeds in Early and Finishing Growth Stages." Animals 12, no. 13 (June 22, 2022): 1607. http://dx.doi.org/10.3390/ani12131607.
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The gut microbiota affects the metabolism, health and growth rate of pigs. Understanding the characteristics of gut microbiota of different pig breeds at each growth stage will enable the design of individualized feeding strategies. The present study aimed to compare the growth curves and development patterns of pigs of three different breeds (Duroc, Landrace and Yorkshire) using the mathematical models Gompertz, Logistic, Von Bertalanffy and Richards. For Duroc pigs, the Gompertz model showed the highest prediction accuracy (R2 = 0.9974). In contrast, the best models for Landrace and Yorkshire pigs were Richards (R2 = 0.9986) and Von Bertalanffy (R2 = 0.9977), respectively. Path analysis showed that body length (path coefficient = 0.507) and chest circumference (path coefficient = 0.532) contributed more significantly to the body weight of pigs at the early growth stage, while hip circumference (path coefficient = 0.312) had a greater influence on pig body weight in the late growth stage. Moreover, the composition of the gut microbiota of pigs at two growth stages (60 kg of body weight in the early growth stage and 120 kg in the finishing stage) was studied using 16S rRNA sequencing technology. Variations in gut microbiota composition of pigs at different growth stages were observed. KEGG pathway enrichment analysis of annotated metagenomes revealed that protein synthesis and amino acid metabolism pathways were significantly enriched in pigs at the early growth stage, which may be related to nutritional requirements of pigs during this stage. This study confirmed longitudinal variation in the gut microbiota of pigs pertaining to age as well as lateral variation related to pig breed. The present findings expand the current understanding of the variations in swine gut microbiota during production stages.