Relevant bibliographies by topics / Computer aided diagnosis tools

Academic literature on the topic 'Computer aided diagnosis tools'

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Published: 25 May 2024

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Journal articles on the topic "Computer aided diagnosis tools"

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Mun, Seong K., and Dow-Mu Koh. "Special Issue: “Machine Learning for Computer-Aided Diagnosis in Biomedical Imaging”." Diagnostics 12, no. 6 (May 27, 2022): 1331. http://dx.doi.org/10.3390/diagnostics12061331.

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The radiology imaging community has been developing computer-aided diagnosis (CAD) tools since the early 1990s before the imagination of artificial intelligence (AI) fueled many unbound healthcare expectations and other industries [...]
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Ioanovici, Andrei-Constantin, Andrei-Marian Feier, Ioan Țilea, and Daniela Dobru. "Computer-Aided Diagnosis in Colorectal Cancer: Current Concepts and Future Prospects." Journal of Interdisciplinary Medicine 2, no. 3 (September 1, 2017): 245–49. http://dx.doi.org/10.1515/jim-2017-0057.

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Abstract Colorectal cancer is an important health issue, both in terms of the number of people affected and the associated costs. Colonoscopy is an important screening method that has a positive impact on the survival of patients with colorectal cancer. The association of colonoscopy with computer-aided diagnostic tools is currently under researchers’ focus, as various methods have already been proposed and show great potential for a better management of this disease. We performed a review of the literature and present a series of aspects, such as the basics of machine learning algorithms, different computational models as well as their benchmarks expressed through measurements such as positive prediction value and accuracy of detection, and the classification of colorectal polyps. Introducing computer-aided diagnostic tools can help clinicians obtain results with a high degree of confidence when performing colonoscopies. The growing field of machine learning in medicine will have a big impact on patient management in the future.
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Molino, F., D. Furia, F. Bar, S. Battista, N. Cappello, and G. Molino. "Computer-Aided Diagnosis in Jaundice: Comparison of Knowledge-based and Probabilistic Approaches." Methods of Information in Medicine 35, no. 01 (January 1996): 41–51. http://dx.doi.org/10.1055/s-0038-1634634.

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AbstractThe study reported in this paper is aimed at evaluating the effectiveness of a knowledge-based expert system (ICTERUS) in diagnosing jaundiced patients, compared with a statistical system based on probabilistic concepts (TRIAL). The performances of both systems have been evaluated using the same set of data in the same number of patients. Both systems are spin-off products of the European project Euricterus, an EC-COMACBME Project designed to document the occurrence and diagnostic value of clinical findings in the clinical presentation of jaundice in Europe, and have been developed as decision-making tools for the identification of the cause of jaundice based only on clinical information and routine investigations. Two groups of jaundiced patients were studied, including 500 (retrospective sample) and 100 (prospective sample) subjects, respectively. All patients were independently submitted to both decision-support tools. The input of both systems was the data set agreed within the Euricterus Project. The performances of both systems were evaluated with respect to the reference diagnoses provided by experts on the basis of the full clinical documentation. Results indicate that both systems are clinically reliable, although the diagnostic prediction provided by the knowledge-based approach is slightly better.
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Bartolini, Ilaria, and Andrea Di Luzio. "CAT-CAD: A Computer-Aided Diagnosis Tool for Cataplexy." Computers 10, no. 4 (April 13, 2021): 51. http://dx.doi.org/10.3390/computers10040051.

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Narcolepsy with cataplexy is a severe lifelong disorder characterized, among others, by sudden loss of bilateral face muscle tone triggered by emotions (cataplexy). A recent approach for the diagnosis of the disease is based on a completely manual analysis of video recordings of patients undergoing emotional stimulation made on-site by medical specialists, looking for specific facial behavior motor phenomena. We present here the CAT-CAD tool for automatic detection of cataplexy symptoms, with the double aim of (1) supporting neurologists in the diagnosis/monitoring of the disease and (2) facilitating the experience of patients, allowing them to conduct video recordings at home. CAT-CAD includes a front-end medical interface (for the playback/inspection of patient recordings and the retrieval of videos relevant to the one currently played) and a back-end AI-based video analyzer (able to automatically detect the presence of disease symptoms in the patient recording). Analysis of patients’ videos for discovering disease symptoms is based on the detection of facial landmarks, and an alternative implementation of the video analyzer, exploiting deep-learning techniques, is introduced. Performance of both approaches is experimentally evaluated using a benchmark of real patients’ recordings, demonstrating the effectiveness of the proposed solutions.
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Jiménez-Gaona, Yuliana, María José Rodríguez-Álvarez, and Vasudevan Lakshminarayanan. "Deep-Learning-Based Computer-Aided Systems for Breast Cancer Imaging: A Critical Review." Applied Sciences 10, no. 22 (November 23, 2020): 8298. http://dx.doi.org/10.3390/app10228298.

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This paper provides a critical review of the literature on deep learning applications in breast tumor diagnosis using ultrasound and mammography images. It also summarizes recent advances in computer-aided diagnosis/detection (CAD) systems, which make use of new deep learning methods to automatically recognize breast images and improve the accuracy of diagnoses made by radiologists. This review is based upon published literature in the past decade (January 2010–January 2020), where we obtained around 250 research articles, and after an eligibility process, 59 articles were presented in more detail. The main findings in the classification process revealed that new DL-CAD methods are useful and effective screening tools for breast cancer, thus reducing the need for manual feature extraction. The breast tumor research community can utilize this survey as a basis for their current and future studies.
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Lee, Juhun, Robert M. Nishikawa, Ingrid Reiser, and John M. Boone. "Optimal reconstruction and quantitative image features for computer-aided diagnosis tools for breast CT." Medical Physics 44, no. 5 (April 13, 2017): 1846–56. http://dx.doi.org/10.1002/mp.12214.

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Ribeiro, Ricardo T., Rui Tato Marinho, and J. Miguel Sanches. "An Ultrasound-Based Computer-Aided Diagnosis Tool for Steatosis Detection." IEEE Journal of Biomedical and Health Informatics 18, no. 4 (July 2014): 1397–403. http://dx.doi.org/10.1109/jbhi.2013.2284785.

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G. P, Vishnu Prasad, Kurapati Vishnu Sai Reddy, A. M. Kiruthik, and Dr J. Arun Nehru. "Prediction of Kidney Stones Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 1037–44. http://dx.doi.org/10.22214/ijraset.2022.42416.

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Abstract: Kidney stones are a prevalent disease all over the world, resulting in many of us being rushed to the hospital in excruciating pain. Calculus illness is diagnosed using a variety of imaging modalities. For the interpretation and thorough diagnosis of the photos, specialists are required. Computer-aided diagnosis systems are practical ways that can be utilized as supplemental tools to aid clinicians in their diagnosis. During this project, the deep learning (DL) technique was used to propose an automatic diagnosis of kidney stones using coronal X-ray (CT) pictures, which has made a great advances in the field of AI. Keywords: Kidney stone, medical image, Deep learning, Computed tomography
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Santos, Marcel Koenigkam, José Raniery Ferreira Júnior, Danilo Tadao Wada, Ariane Priscilla Magalhães Tenório, Marcello Henrique Nogueira Barbosa, and Paulo Mazzoncini de Azevedo Marques. "Artificial intelligence, machine learning, computer-aided diagnosis, and radiomics: advances in imaging towards to precision medicine." Radiologia Brasileira 52, no. 6 (December 2019): 387–96. http://dx.doi.org/10.1590/0100-3984.2019.0049.

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Abstract The discipline of radiology and diagnostic imaging has evolved greatly in recent years. We have observed an exponential increase in the number of exams performed, subspecialization of medical fields, and increases in accuracy of the various imaging methods, making it a challenge for the radiologist to “know everything about all exams and regions”. In addition, imaging exams are no longer only qualitative and diagnostic, providing now quantitative information on disease severity, as well as identifying biomarkers of prognosis and treatment response. In view of this, computer-aided diagnosis systems have been developed with the objective of complementing diagnostic imaging and helping the therapeutic decision-making process. With the advent of artificial intelligence, “big data”, and machine learning, we are moving toward the rapid expansion of the use of these tools in daily life of physicians, making each patient unique, as well as leading radiology toward the concept of multidisciplinary approach and precision medicine. In this article, we will present the main aspects of the computational tools currently available for analysis of images and the principles of such analysis, together with the main terms and concepts involved, as well as examining the impact that the development of artificial intelligence has had on radiology and diagnostic imaging.
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Owais, Muhammad, Muhammad Arsalan, Tahir Mahmood, Jin Kyu Kang, and Kang Ryoung Park. "Automated Diagnosis of Various Gastrointestinal Lesions Using a Deep Learning–Based Classification and Retrieval Framework With a Large Endoscopic Database: Model Development and Validation." Journal of Medical Internet Research 22, no. 11 (November 26, 2020): e18563. http://dx.doi.org/10.2196/18563.

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Background The early diagnosis of various gastrointestinal diseases can lead to effective treatment and reduce the risk of many life-threatening conditions. Unfortunately, various small gastrointestinal lesions are undetectable during early-stage examination by medical experts. In previous studies, various deep learning–based computer-aided diagnosis tools have been used to make a significant contribution to the effective diagnosis and treatment of gastrointestinal diseases. However, most of these methods were designed to detect a limited number of gastrointestinal diseases, such as polyps, tumors, or cancers, in a specific part of the human gastrointestinal tract. Objective This study aimed to develop a comprehensive computer-aided diagnosis tool to assist medical experts in diagnosing various types of gastrointestinal diseases. Methods Our proposed framework comprises a deep learning–based classification network followed by a retrieval method. In the first step, the classification network predicts the disease type for the current medical condition. Then, the retrieval part of the framework shows the relevant cases (endoscopic images) from the previous database. These past cases help the medical expert validate the current computer prediction subjectively, which ultimately results in better diagnosis and treatment. Results All the experiments were performed using 2 endoscopic data sets with a total of 52,471 frames and 37 different classes. The optimal performances obtained by our proposed method in accuracy, F1 score, mean average precision, and mean average recall were 96.19%, 96.99%, 98.18%, and 95.86%, respectively. The overall performance of our proposed diagnostic framework substantially outperformed state-of-the-art methods. Conclusions This study provides a comprehensive computer-aided diagnosis framework for identifying various types of gastrointestinal diseases. The results show the superiority of our proposed method over various other recent methods and illustrate its potential for clinical diagnosis and treatment. Our proposed network can be applicable to other classification domains in medical imaging, such as computed tomography scans, magnetic resonance imaging, and ultrasound sequences.
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Dissertations / Theses on the topic "Computer aided diagnosis tools"

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Hoh, See Min. "Condition monitoring and fault diagnosis for CNC machine tools." Thesis, Cardiff University, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.295120.

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Coxon, Andrew. "Computer aided analysis of paraspinal electromyography." Thesis, Teesside University, 2013. http://hdl.handle.net/10149/301616.

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Back pain is responsible for British employees taking 5 million sick days per year. Low back pain (LBP) has a controversial aetiology, with 95% of cases caused by mechanical, non-pathological causes. Current medical treatment for mechanical LBP is an exercise regime designed to restore lumbar stability. Unfortunately this is often a painful process, and therefore difficult to complete. Electromyography (EMG) variables have been shown to be able to discriminate between subjects with and without mechanical LBP. If these variables could be shown to have discriminatory abilities before the actual onset of LBP they could be used to predict future episodes of LBP in currently otherwise asymptomatic individuals and allow the rehabilitation process to begin before the onset of symptoms. However a number of problems persist with EMG measurement. The test must be administered under closely controlled conditions in order to record clean signals, and interpretation of this data requires special tools and training. This thesis aims to make contributions in three main areas; Automated Analysis: Manual analysis of a large store of EMG raw data files is a time consuming process. If outcome variables that require manual interpretation are included this effect is magnified, with necessary questions being raised as to the accuracy and consistency levels that can be maintained. A successfully implemented automated system would reduce analysis time and improve confidence in the outcome variables recorded. Investigations will also be carried out into the addition of error detection and correction algorithms that could be performed during the analysis procedure. ECG Contamination Removal: Previous studies have identified ECG as a potential source of contamination of lumbar EMG signals. Compensation for this effect is non-trivial as the ECG frequencies overlap an area of interest in the EMG spectrum, and the ECG signal characteristics would change over a fatiguing EMG test. The Independent Component Analysis method will be used to attempt to extract and remove the ECG component of a recorded signal whilst preserving the underlying EMG data. If this is successful an analysis of the effect that removing ECG contamination has on EMG outcome variables will be presented. Colour Map Diagnostic Method: Colour maps are an excellent method of presenting a large amount of signal data to a researcher, and have been used to discriminate between LBP and non-LBP subjects. The usefulness of this diagnostic display too has been somewhat limited however by the difficulty in producing such maps. Investigations will be carried out into methods that will be able to quickly and accurately produce these colour maps to the same specification as earlier studies. Colour maps of subjects that did not report LBP at the time of testing, but who then did report LBP at their next presentation, will be examined to assess whether or not EMG colour maps can be used as a predictor for low back pain.
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Luther, Ilse. "Semen characteristics of free-ranging African elephants (Loxodonta africana) and Southern white rhinoceros (Ceratotherium simum simum) using Computer-aided sperm analysis, Electron microscopy and Genomics as diagnostic tools." University of the Western Cape, 2016. http://hdl.handle.net/11394/5443.

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Philosophiae Doctor - PhD
The survival of free-ranging (in situ) African elephant and Southern white rhinoceros populations are currently being challenged on a daily basis in Africa. Reproductive health is considered a vital component of species conservation. Conservation of the last mega land mammals may ultimately require intervention by breeding management or combined with assisted reproductive technologies (ART). There is a strong case for gathering baseline information, both physiological and biological, of any species, as opportunities arise. During this study a total number of 21 ejaculates collected over two seasons from 12 free-ranging African elephant bulls were characterised, as well as 10 ejaculates collected from 10 free-ranging Southern white rhinoceros bulls from two populations. Ejaculates were collected from adult bulls by means of electroejaculation under anaesthesia. Routine semen analysis was combined with Computer-aided sperm analysis (CASA), Computer-aided sperm morphology analysis (CASMA), Transmission electron microscopy (TEM) and Genomics as diagnostic tools. Additionally, sperm functionality within different media was investigated and sperm subpopulation classification according to the motion pattern displayed. The results presented is based on the evaluation and classification of ≈ 45 000 individual African elephant spermatozoa and ≈ 18 000 individual Southern white rhinoceros spermatozoa. The average elephant ejaculate contained a total number of 47 x 10⁹ spermatozoa (volume of 56 ± 38mL x concentration of 818 ± 750 x 10⁶/mL) that recorded a total motility of 81 ± 29% of which 62 ± 26% were progressively motile. CASA recorded velocities for curvilinear velocity (VCL 241 ± 58μm/s), straight-line velocity (VSL 173 ± 181μm/s) and average path velocity (VAP 201 ± 54μm/s), and kinematics at straightness of track (STR 86 ± 85%), linearity of track (LIN 67 ± 16%), amplitude of lateral head displacement (ALH 4 ± 0.75μm) and beat cross frequency (BCF 21 ± 3Hz). Structural analysis revealed 68 ± 11% of the spermatozoa were viable (intact plasma membrane) and 77 ± 11% maintained acrosome integrity. Ejaculates contained 55 ± 14% morphologically normal spermatozoa, CASMA measured sperm head lengths at 6.83 ± 0.26μm and width 3.32 ± 0.18μm (total head area of 20.17 ± 1.96μm²) of which 38.95 ± 0.92% is covered by an acrosomal cap. The average rhinoceros ejaculate contained a total number of 1.1 x 10⁹ spermatozoa (volume of 24 ± 24mL x concentration of 83 ± 96 x 10⁶/mL) that recorded a total motility at 82 ± 8% of which 28 ± 23% were progressively motile. CASA recorded velocities for VCL (85 ± 29μm/s), VSL (44 ± 25μm/s) and VAP (69 ± 30μm/s, and kinematics at STR (63 ± 14%), LIN (51 ± 16%), ALH (2 ± 0.16μm) and BCF (16 ± 6Hz). Structural analysis revealed 73 ± 10% of the spermatozoa were viable (intact plasma membrane) and 76 ± 4% maintained acrosome integrity. Ejaculates contained 62 ± 14% morphologically normal spermatozoa, CASMA measured sperm head lengths at 5.5 ± 0.17μm and width 2.9 ± 0.19μm (total head area of 14.8 ± 1.43μm²) of which 36.3 ± 0.59% is covered by an acrosomal cap. Based on a Boolean argument and CASA data exploration it was possible to derive elephant and rhinoceros CASA cut-off criteria to sort between activated and hyperactivated motile spermatozoa. For the genomic component of this study, the CatSper1 (Loxodonta africana) gene was identified,sequenced and verified in a free-ranging (natural) African elephant population. Multivariate analysis(MVA) was applied to examine the associations between the semen and sperm parameters and the traits they accounted for in this study. Our understanding of wildlife reproductive sciences can substantially progress as the analytical techniques applied and the combination thereof is expanded. This investigation presents a new set of comprehensive semen and sperm threshold values for future investigations.
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Borelli, João Eduardo. "Diagnóstico do estado de desgaste de ferramentas para o monitoramento de condições de usinagem de alto desempenho." Universidade de São Paulo, 2000. http://www.teses.usp.br/teses/disponiveis/18/18133/tde-14112017-104229/.

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Durante o processo de usinagem, o conhecimento da temperatura é um dos fatores mais importante na análise do estado da ferramenta. Permite o controle dos fatores mais importantes que influenciam, no uso, na vida e no desgaste da ferramenta. A temperatura na região de contato entre a peça e a ferramenta é resultante do processo de remoção de material durante a operação de corte e é difícil de se obter uma vez que, ou a peça, ou a ferramenta estão em movimento. Uma maneira de se medir a temperatura nessa situação é detectando a radiação de infravermelho. Este trabalho tem objetivo de apresentar uma nova metodologia de diagnóstico e monitoramento de operações de usinagem com o uso de imagens de infravermelho. A imagem de infravermelho fornece um mapa em tons de cinza da temperatura dos elementos participantes do processo: ferramenta, peça e cavaco. Cada tom de cinza na imagem corresponde a uma temperatura para cada material. A correspondência entre tons de cinza e a temperatura é dada pela prévia calibração da câmera de infravermelho para os materiais participantes do processo. O sistema desenvolvido neste trabalho usa uma câmera de infravermelho, uma frame grabber e um software composto por 3 módulos: o primeiro módulo faz a aquisição da imagem de infravermelho e o processamento; o segundo módulo faz a extração e o cálculo do vetor de características das imagens. Finalmente o terceiro módulo usa um algoritmo fuzzy e fornece como saída o diagnóstico do estado da ferramenta.
During machining process the temperature knowledge is one of the most important factors in tool analysis. It allows to control main factors that influence tool use, life time and wear. The temperature in the contact area between the work piece and the tool is resulting from the material remova! in cutting operation and it is too difficult to be obtained because the tool, or the work piece is in motion. One way to measure the temperature in this situation is detecting the infrared radiation. This work presents a new methodology for diagnosis and monitoring of machining processes with the use of infrared images. The infrared image provides a map in gray tones of the elements temperature in the process: tool, work piece and chips. Each gray tone corresponds to a certain temperature for each one of those materials and the relationship between the gray tones and the temperature is goven by previous infrared camera calibration. The system developed in this work uses an infrared camera, a frame grabber board and a software composed by three modules. The first module provides the image acquisition and processing. The second one does the image feature extraction and calculates the feature vector. Finally, the third module uses fuzzy logic to evaluate the feature vector and to supply the tool state diagnostic as output.
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Elter, Matthias. "Computer-aided diagnosis of breast cancer." Tönning Lübeck Marburg Der Andere-Verl, 2010. http://d-nb.info/1001110773/04.

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Nakamura, Yoshihiko, Takayuki Kitasaka, Kensaku Mori, and Yasuhito Suenaga. "COMPUTER AIDED DIAGNOSIS FOR ABDOMINAL SURGICAL PLANNING." INTELLIGENT MEDIA INTEGRATION NAGOYA UNIVERSITY / COE, 2006. http://hdl.handle.net/2237/10470.

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Malone, John Philip. "Computer-aided diagnosis of diffuse lung disease." Thesis, University of Bristol, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.440143.

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Tembey, Mugdha. "Computer-Aided Diagnosis for Mammographic Microcalcification Clusters." [Tampa, Fla.] : University of South Florida, 2003. http://purl.fcla.edu/fcla/etd/SFE0000168.

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PUTZU, LORENZO. "Computer aided diagnosis algorithms for digital microscopy." Doctoral thesis, Università degli Studi di Cagliari, 2016. http://hdl.handle.net/11584/266877.

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Automatic analysis and information extraction from an image is still a highly chal- lenging research problem in the computer vision area, attempting to describe the image content with computational and mathematical techniques. Moreover the in- formation extracted from the image should be meaningful and as most discrimi- natory as possible, since it will be used to categorize its content according to the analysed problem. In the Medical Imaging domain this issue is even more felt because many important decisions that affect the patient care, depend on the use- fulness of the information extracted from the image. Manage medical image is even more complicated not only due to the importance of the problem, but also because it needs a fair amount of prior medical knowledge to be able to represent with data the visual information to which pathologist refer. Today medical decisions that impact patient care rely on the results of laboratory tests to a greater extent than ever before, due to the marked expansion in the number and complexity of offered tests. These developments promise to improve the care of patients, but the more increase the number and complexity of the tests, the more increases the possibility to misapply and misinterpret the test themselves, leading to inappropriate diagnosis and therapies. Moreover, with the increased number of tests also the amount of data to be analysed increases, forcing pathologists to devote much time to the analysis of the tests themselves rather than to patient care and the prescription of the right therapy, especially considering that most of the tests performed are just check up tests and most of the analysed samples come from healthy patients. Then, a quantitative evaluation of medical images is really essential to overcome uncertainty and subjectivity, but also to greatly reduce the amount of data and the timing for the analysis. In the last few years, many computer assisted diagno- sis systems have been developed, attempting to mimic pathologists by extracting features from the images. Image analysis involves complex algorithms to identify and characterize cells or tissues using image pattern recognition technology. This thesis addresses the main problems associated to the digital microscopy analysis in histology and haematology diagnosis, with the development of algorithms for the extraction of useful information from different digital images, but able to distinguish different biological structures in the images themselves. The proposed methods not only aim to improve the degree of accuracy of the analysis, and reducing time, if used as the only means of diagnoses, but also they can be used as intermediate tools for skimming the number of samples to be analysed directly from the pathologist, or as double check systems to verify the correct results of the automated facilities used today.
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Mori, Kensaku. "Advances in Computer Aided Diagnosis and Computer Assisted Surgery." INTELLIGENT MEDIA INTEGRATION NAGOYA UNIVERSITY / COE, 2004. http://hdl.handle.net/2237/10452.

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Books on the topic "Computer aided diagnosis tools"

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Elseid, Arwa Ahmed Gasm, and Alnazier Osman Mohammed Hamza. Computer-Aided Glaucoma Diagnosis System. First edition. | Boca Raton, FL : CRC Press, 2020.: CRC Press, 2020. http://dx.doi.org/10.1201/9780367406288.

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K, Law K., ed. Computer-aided sculpture. Cambridge [England]: Cambridge University Press, 1989.

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Das, Rik, Sudarshan Nandy, and Siddhartha Bhattacharyya. Disruptive Trends in Computer Aided Diagnosis. New York: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003045816.

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Su, Ruidan, and Han Liu, eds. Medical Imaging and Computer-Aided Diagnosis. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5199-4.

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Hastie, Trevor. Computer-aided diagnosis of mammographic masses. [Toronto]: University of Toronto, Dept. of Statistics, 1996.

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Lung imaging and computer aided diagnosis. Boca Raton, FL: CRC Press, 2012.

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Su, Ruidan, Yudong Zhang, Han Liu, and Alejandro F Frangi, eds. Medical Imaging and Computer-Aided Diagnosis. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-16-6775-6.

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Krishnamoorthy, C. S. Computer aided design: Software and analytical tools. New Delhi: Narosa Pub. House, 1991.

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Krishnamoorthy, C. S. Computer aided design: Software and analytical tools. Berlin: Springer-Verlag, 1992.

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Computer aided design: Software and analytical tools. 2nd ed. Great Britain: Alpha Science Intl Ltd, 2005.

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Book chapters on the topic "Computer aided diagnosis tools"

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Katis, Andreas, Anastasia Mavridou, Dimitra Giannakopoulou, Thomas Pressburger, and Johann Schumann. "Capture, Analyze, Diagnose: Realizability Checking Of Requirements in FRET." In Computer Aided Verification, 490–504. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13188-2_24.

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AbstractRequirements formalization has become increasingly popular in industrial settings as an effort to disambiguate designs and optimize development time and costs for critical system components. Formal requirements elicitation also enables the employment of analysis tools to prove important properties, such as consistency and realizability. In this paper, we present the realizability analysis framework that we developed as part of the Formal Requirements Elicitation Tool (FRET). Our framework prioritizes usability, and employs state-of-the-art analysis algorithms that support infinite theories. We demonstrate the workflow for realizability checking, showcase the diagnosis process that supports visualization of conflicts between requirements and simulation of counterexamples, and discuss results from industrial-level case studies.
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Mori, Kazuo. "Case Example 6: In-process diagnosis of tool failures in milling." In Computer-aided Maintenance, 356–78. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-5305-2_17.

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Gernert, Regine, and Peter John. "Design of Competence Promoting Multi-Agent-Systems to Support the User in Fault Diagnosis of CNC-Machine Tools." In Computer Aided Systems Theory - EUROCAST’99, 201–8. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/10720123_17.

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Kvak, Daniel, Eva Březinová, Marek Biroš, and Robert Hrubý. "Synthetic Data as a Tool to Combat Racial Bias in Medical AI: Utilizing Generative Models for Optimizing Early Detection of Melanoma in Fitzpatrick Skin Types IV–VI." In Medical Imaging and Computer-Aided Diagnosis, 317–30. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-16-6775-6_26.

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Singh, Vedpal, Irraivan Elamvazuthi, Varun Jeoti, John George, Norashikin Yahya, and Dileep Kumar. "Ultrasound Based Three Dimensional Computer Aided Diagnosis (CAD) Tool for the Diagnosis of Anterior Talofibular Ligament." In Medical Imaging in Clinical Applications, 213–35. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-33793-7_10.

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Heinonen, T., T. Arola, A. Kalliokoski, P. Dastidar, M. Rossi, S. Soimakallio, J. Hyttinen, and H. Eskola. "Computer Aided Diagnosis Tool for the Segmentation and Texture Analysis of Medical Images." In IFMBE Proceedings, 274–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03879-2_77.

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Banerjee, Neha, Rachana Sathish, and Debdoot Sheet. "Deep Neural Architecture for Localization and Tracking of Surgical Tools in Cataract Surgery." In Computer Aided Intervention and Diagnostics in Clinical and Medical Images, 31–38. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-04061-1_4.

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Pirovano, Antoine, Hippolyte Heuberger, Sylvain Berlemont, Saïd Ladjal, and Isabelle Bloch. "Improving Interpretability for Computer-Aided Diagnosis Tools on Whole Slide Imaging with Multiple Instance Learning and Gradient-Based Explanations." In Interpretable and Annotation-Efficient Learning for Medical Image Computing, 43–53. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61166-8_5.

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Pancerz, Krzysztof, Olga Mich, Andrzej Burda, and Jerzy Gomuła. "A Tool for Computer-Aided Diagnosis of Psychological Disorders Based on the MMPI Test: An Overview." In Studies in Computational Intelligence, 201–13. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19147-8_12.

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Bricault, Ivan, Ron Kikinis, Eric van Sonnenberg, Kemal Tuncali, and Stuart G. Silverman. "3D Analysis of Radiofrequency-Ablated Tumors in Liver: A Computer-Aided Diagnosis Tool for Early Detection of Local Recurrences." In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2004, 1042–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30136-3_129.

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Conference papers on the topic "Computer aided diagnosis tools"

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Bento, Mariana P., Roberto M. Souza, Richard Frayne, and Marina Salluzzi. "Reliability of computer-aided diagnosis tools with multi-center MR datasets: impact of training protocol." In Computer-Aided Diagnosis, edited by Horst K. Hahn and Kensaku Mori. SPIE, 2019. http://dx.doi.org/10.1117/12.2512819.

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Guan, Shuyue, Ravi K. Samala, Arian Arab, and Weijie Chen. "MISS-tool: medical image segmentation synthesis tool to emulate segmentation errors." In Computer-Aided Diagnosis, edited by Khan M. Iftekharuddin and Weijie Chen. SPIE, 2023. http://dx.doi.org/10.1117/12.2653650.

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Vandewiele, Stijn, Jonas De Vylder, Bart Diricx, Edward Sandra, and Tom Kimpe. "Open-source tool for model performance analysis for subpopulations." In Computer-Aided Diagnosis, edited by Khan M. Iftekharuddin and Weijie Chen. SPIE, 2023. http://dx.doi.org/10.1117/12.2653453.

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Burgon, Alexis, Nicholas Petrick, Berkman Sahiner, Gene Pennello, Kenny Cha, and Ravi K. Samala. "A tool for the assessment of AI generalizability via decision space composition." In Computer-Aided Diagnosis, edited by Susan M. Astley and Weijie Chen. SPIE, 2024. http://dx.doi.org/10.1117/12.3008580.

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Jahani, Nariman, Eric Cohen, Meng-Kang Hsieh, Susan P. Weinstein, Lauren Pantalone, Christos Davatzikos, and Despina Kontos. "Deformable image registration as a tool to improve survival prediction after neoadjuvant chemotherapy for breast cancer: results from the ACRIN 6657/I-SPY-1 trial." In Computer-Aided Diagnosis, edited by Kensaku Mori and Nicholas Petrick. SPIE, 2018. http://dx.doi.org/10.1117/12.2293720.

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Gadermayr, Michael, Hubert Kogler, Andreas Uhl, and Andreas Vecsei. "Comparing endoscopic imaging configurations in computer-aided celiac disease diagnosis." In 2015 International Conference on Image Processing Theory, Tools and Applications (IPTA). IEEE, 2015. http://dx.doi.org/10.1109/ipta.2015.7367184.

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Li, Lin, Qizhi Zhang, Yihua Ding, Huabei Jiang, Bruce T. Thiers, and James Z. Wang. "A Computer-Aided Spectroscopic System for Early Diagnosis of Melanoma." In 2013 IEEE 25th International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2013. http://dx.doi.org/10.1109/ictai.2013.31.

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Gorriz, J. M., J. Ramirez, A. Lassl, D. Salas-Gonzalez, E. W. Lang, C. G. Puntonet, I. Alvarez, M. Lopez, and M. Gomez-Rio. "Automatic computer aided diagnosis tool using component-based SVM." In 2008 IEEE Nuclear Science Symposium and Medical Imaging conference (2008 NSS/MIC). IEEE, 2008. http://dx.doi.org/10.1109/nssmic.2008.4774255.

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Wang, Lulu, Ahmed Al-Jumaily, and Andrew Kilding. "Computer-Aided Diagnostic Tool for Lung Diseases." In ASME 2015 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/imece2015-50550.

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Abstract:
Exercise-induced bronchoconstriction (EIB) and Exercise-induced asthma (EIA) are used synonymously to describe acute lung airway narrowing occurring during and/or after exercise. Tools usually used by health practitioners to assess the severity of EIB or EIA symptoms include bronchodilator tests and various bronchial provocation procedures including spirometer and eucapnic voluntary hyperpnoea (EVH) challenge system, there is no tool that is able to monitor the severity of acute lung airway narrowing. This study aims to design a graphical user interface (GUI) to monitor the severity of acute lung airway narrowing using MATLAB software. The GUI will present the measurement data into a simple and user-friendly program consisting of patient information, EVH test analysis and detection of EIA and EIB. Success of this project widens the usage of EVH challenge in medical areas and the GUI may serve as a new clinical computer-aided diagnostic tool to help healthcare professionals non-invasively monitor the severity of asthma, EIA and EIB.
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Zhao, Zheen, David K. Wallace, Sharon F. Freedman, and Stephen R. Aylward. "A tool for computer-aided diagnosis of retinopathy of prematurity." In Medical Imaging, edited by Maryellen L. Giger and Nico Karssemeijer. SPIE, 2008. http://dx.doi.org/10.1117/12.769030.

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Reports on the topic "Computer aided diagnosis tools"

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Floyd, Carey E. Computer Aided Breast Cancer Diagnosis. Fort Belvoir, VA: Defense Technical Information Center, October 1996. http://dx.doi.org/10.21236/ada325798.

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Floyd, Carey E. Computer Aided Breast Cancer Diagnosis. Fort Belvoir, VA: Defense Technical Information Center, October 2000. http://dx.doi.org/10.21236/ada392958.

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Floyd, Carey E. Computer Aided Breast Cancer Diagnosis. Fort Belvoir, VA: Defense Technical Information Center, October 1999. http://dx.doi.org/10.21236/ada383108.

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Jiang, Yulei. Computer-Aided Diagnosis of Digital Mammograms. Fort Belvoir, VA: Defense Technical Information Center, June 2001. http://dx.doi.org/10.21236/ada396524.

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Jiang, Yulei. Computer-Aided Diagnosis of Digital Mammograms. Fort Belvoir, VA: Defense Technical Information Center, June 2004. http://dx.doi.org/10.21236/ada431258.

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Jiang, Yulei. Computer-Aided Diagnosis of Breast Lesions. Fort Belvoir, VA: Defense Technical Information Center, June 2002. http://dx.doi.org/10.21236/ada410986.

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Jiang, Yulei. Computer-Aided Diagnosis of Digital Mammograms. Fort Belvoir, VA: Defense Technical Information Center, June 2003. http://dx.doi.org/10.21236/ada421590.

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Kupinski, Matthew A. Investigation of Genetic Algorithms for Computer-Aided Diagnosis. Fort Belvoir, VA: Defense Technical Information Center, October 2000. http://dx.doi.org/10.21236/ada393995.

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Chan, Heang P. Digital Mammography: Advanced Computer-Aided Breast Cancer Diagnosis. Fort Belvoir, VA: Defense Technical Information Center, May 2003. http://dx.doi.org/10.21236/ada420159.

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Kupinski, Matthew A. Investigation of Genetic Algorithms for Computer-Aided Diagnosis. Fort Belvoir, VA: Defense Technical Information Center, October 1999. http://dx.doi.org/10.21236/ada391457.

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