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Добірка наукової літератури з теми "LUNG CANCER DETECTION"

Автор: Grafiati
Опубліковано: 11 вересня 2023

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Статті в журналах з теми "LUNG CANCER DETECTION"

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M, Jerin Jose, Janani B.R, Janani Priya K, Jeevitha J, and Swathi S. "Lung Cancer Detection using Artificial Neural Netw." SIJ Transactions on Computer Science Engineering & its Applications (CSEA) 05, no. 06 (December 27, 2017): 01–05. http://dx.doi.org/10.9756/sijcsea/v5i6/05010050101.

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Reddy, Y. Venkat Sai, G. Chandana, G. Chetan Redddy, Ayush Kumar, Suvarna Kumar, and Dr Syed Siraj Ahmed. "Lung Cancer Detection using YOLO CNN Algorithm." International Journal of Research Publication and Reviews 4, no. 5 (June 2023): 5297–300. http://dx.doi.org/10.55248/gengpi.4.523.43476.

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M, Albertini. "Canine Scent Detection of Lung Cancer: Preliminary Results." Open Access Journal of Veterinary Science & Research 1, no. 4 (2016): 1–5. http://dx.doi.org/10.23880/oajvsr-16000118.

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Анотація:
Several researches have evidenced that cancer cells can produce volatile organic compounds (VOCs) which are released not only in breath but also in other organic fluids, such as blood and urine. This study has evaluated the olfactory capability of trained dogs to detect human lung cancer VOCs in urine. We recruited 150 subjects from European Institute of Oncology (IEO) divided into three groups: 57 patients with lung cancer (group 1); 38 patients with lung disease, other than cancer (group 2); 55 healthy co ntrol subjects (group 3).The results are referred to the last 45 days of training, and evidenced that dogs reached a mean success rate that exceeded 80%, with a sensitivity of 0,72 and a specificity of 0,94 for two out of three dogs enrolled. The important novelty is that dogs can discriminate lung cancer not only from healthy subjects, but also from patients with other lung diseases. The results obtained so far are encouraging and lead us to persevere with the training session in order to improve the succe ss rate, reaching values as close as possible to 100%. If so, we believe that, in the future, dogs may be used to perform early diagnostic tests, useful in improving the chances of survival in cases of human lung cancer.
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Patel, Bindiya, Dr Pankaj Kumar Mishra, and Prof Amit Kolhe. "Lung Cancer Detection on CT Images by using Image Processing." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (April 30, 2018): 2525–31. http://dx.doi.org/10.31142/ijtsrd11674.

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Choe, Wonho, Jeong Don Chae, Byoung-Hoon Lee, Sang-Hoon Kim, So Young Park, Satish Balasaheb Nimse, Junghoon Kim, et al. "9G TestTM Cancer/Lung: A Desirable Companion to LDCT for Lung Cancer Screening." Cancers 12, no. 11 (October 30, 2020): 3192. http://dx.doi.org/10.3390/cancers12113192.

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Анотація:
A complimentary biomarker test that can be used in combination with LDCT for lung cancer screening is highly desirable to improve the diagnostic capacity of LDCT and reduce the false-positive rates. Most importantly, the stage I lung cancer detection rate can be dramatically increased by the simultaneous use of a biomarker test with LDCT. The present study was conducted to evaluate 9G testTM Cancer/Lung’s sensitivity and specificity in detecting Stage 0~IV lung cancer. The obtained results indicate that the 9G testTM Cancer/Lung can detect lung cancer with overall sensitivity and specificity of 75.0% (69.1~80.3) and 97.3% (95.0~98.8), respectively. The detection of stage I, stage II, stage III, and stage IV cancers with sensitivities of 77.5%, 78.1%, 67.4%, and 33.3%, respectively, at the specificity of 97.3% have never been reported before. The receiver operating characteristic curve analysis allowed us to determine the population-weighted AUC of 0.93 (95% CI, 0.91–0.95). These results indicate that the 9G testTM Cancer/Lung can be used in conjunction with LDCT to screen lung cancer. Furthermore, obtained results indicate that the use of 9G testTM Cancer/Lung with LDCT for lung cancer screening can increase stage I cancer detection, which is crucial to improve the currently low 5-year survival rates.
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Yektaei, Homayoon, and Mohammad Manthouri. "DIAGNOSIS OF LUNG CANCER USING MULTISCALE CONVOLUTIONAL NEURAL NETWORK." Biomedical Engineering: Applications, Basis and Communications 32, no. 05 (August 12, 2020): 2050030. http://dx.doi.org/10.4015/s1016237220500301.

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Анотація:
Lung cancer is one of the dangerous diseases that cause huge cancer death worldwide. Early detection of lung cancer is the only possible way to improve a patient’s chance for survival. This study presents an innovative automated diagnosis classification method for Computed Tomography (CT) images of lungs. In this paper, the CT scan of lung images was analyzed with the multiscale convolution. The entire lung is segmented from the CT images and the parameters are calculated from the segmented image. The use of image processing techniques and identifying patterns in the detection of lung cancer from CT images reduces human errors in detecting tumors, and speeds up diagnosis time. Artificial Neural Network (ANN) has been widely used to detect lung cancer, and has significantly reduced the percentage of errors. Therefore, in this paper, Convolution Neural Network (CNN), which is the most effective method, is used for the detection of various types of cancers. This study presents a Multiscale Convolutional Neural Network (MCNN) approach for the classification of tumors. Based on the structure of MCNN, which presents CT picture to several deep convolutional neural networks with different size and resolutions, the classical handcrafted features extraction step is avoided. The proposed approach gives better classification rates than the classical state of the art methods allowing a safer Computer-Aided Diagnosis of pleural cancer. This study reaches a diagnosis accuracy of [Formula: see text] using multiscale convolution technique, which reveals the efficiency of the proposed method.
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Kaur, Jatinder, Ravi Prakash Chaturvedi, and Sameer Asthana. "Lung cancer detection a machine learning approach." YMER Digital 21, no. 03 (March 23, 2022): 337–48. http://dx.doi.org/10.37896/ymer21.03/37.

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Анотація:
In the United States, lung cancer is the largest cause of cancer-related death. Patients with early discovery and diagnosis have a better chance of surviving. Our main focus in this project is on developing a computer-aided diagnostic (CAD) tool for early cancer detection utilizing CT scans as information. The LUNA 16 data set and the kaggle data set are two different data sets that we introduce. In this research, we provide a methodology for predicting whether a patient has cancer using information learned from the LUNA data set on the kaggle data. We discuss pre-processing, lung segmentation, lung nodule segmentation, and finally data classification. We use GPU-enabled clusters to run the methods provided in each step above due to the large amount of data. The results were positive, and the pipeline project is functional that can be used in real-world applications
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Inage, Terunaga, Takahiro Nakajima, Ichiro Yoshino, and Kazuhiro Yasufuku. "Early Lung Cancer Detection." Clinics in Chest Medicine 39, no. 1 (March 2018): 45–55. http://dx.doi.org/10.1016/j.ccm.2017.10.003.

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Smith, Robert A., and Thomas J. Glynn. "Early lung cancer detection." Cancer 89, S11 (December 1, 2000): 2327–28. http://dx.doi.org/10.1002/1097-0142(20001201)89:11+<2327::aid-cncr1>3.0.co;2-r.

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Thanzeem Mohamed Sheriff, S., J. Venkat Kumar, S. Vigneshwaran, Aida Jones, and Jose Anand. "Lung Cancer Detection using VGG NET 16 Architecture." Journal of Physics: Conference Series 2040, no. 1 (October 1, 2021): 012001. http://dx.doi.org/10.1088/1742-6596/2040/1/012001.

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Анотація:
Abstract Cancer is one of the main reason for loss of human life across the world. All the medical practitioners and researchers are dealing with the demanding situations to fight against cancer. Based on the report in 2019 from American Cancer Society, 96,480 deaths are anticipated due to skin cancers, 142,670 deaths are from lung cancers, 42,260 deaths are from breast cancers, 31,620 deaths are from prostate cancers, and 17,760 deaths are from mind cancers. Initial detection of most cancers has the pinnacle precedence for saving the lives. This paper proposed a lung cancer detection using Deep Learning based on VEE NET architecture. This was one of the famous models submitted to ILSVRC-2014. Visual checkup and manual practices are used on this venture for the various types of cancer diagnoses. This guide interpretation of scientific images that needs massive time intake and is notably susceptible to mistakes. Thus, in this project, we apply deep learning algorithms to identify lung cancer and its presence without the need for several consultations from different doctors. This leads to an earlier prediction of the presence of the disease and allows us to take prior actions immediately to avoid further consequences in an effective and cheap manner avoiding human error rate. In this project lung cancer and its presence is determined. A web application is developed as a hospital application where an input x-ray image is given to detect lung cancer.
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Більше джерел

Дисертації з теми "LUNG CANCER DETECTION"

1

田珮芝 and Pui-chi Tin. "Detection of EGFR mutation in lung adenocarcinoma and paired plasma." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2008. http://hub.hku.hk/bib/B40737044.

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Kalubowilage, Madumali. "Liquid biopsies of solid tumors: non-small-cell lung and pancreatic cancer." Diss., Kansas State University, 2017. http://hdl.handle.net/2097/35385.

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Анотація:
Doctor of Philosophy
Department of Chemistry
Stefan H. Bossmann
Cancer is a group of diseases that are characterized by uncontrolled growth and spread of cells. In order to treat cancer successfully, it is important to diagnose cancers in their early stages, because survival often depends on the stage of cancer detection. For that purpose, highly sensitive and selective methods must be developed, taking advantage of suitable biomarkers. The expression levels of proteases differ from one cancer type to the other, because different cancers arise from different cell types. According to the literature, there are significant differences between the protease expression levels of cancer patients and healthy people, because solid tumors rely on proteases for survival, angiogenesis and metastasis. Development of fluorescence-based nanobiosensors for the early detection of pancreatic cancer and non-small-cell lung cancer is discussed in this thesis. The nanobiosensors are capable of detecting protease/arginase activities in serum samples over a broad range. The functionality of the nanobiosensor is based on Förster resonance energy transfer and surface energy transfer mechanisms. The nanobiosensors for protease detection feature dopamine-coated Fe/Fe₃O₄ nanoparticles, consensus (cleavage) peptide sequences, meso-tetra(4-carboxyphenyl)porphine (TCPP), and cyanine 5.5. The consensus peptide sequences were synthesized by solid-supported peptide synthesis. In this thesis, improved consensus sequences were used, which permit faster synthesis and higher signal intensities. TCPP, which is the fluorophore of the nanoplatform, was connected to the N-terminal end of the oligopeptides while it was still on the resin. After the addition of TCPP, the TCPP-oligopeptide was cleaved off the resin and linked to the primary amine groups of Fe/Fe₃O₄-bound via a stable amide bond. In the presence of a particular protease, the consensus sequences attached to the nanoparticle can be cleaved and release TCPP to the aqueous medium. Upon releasing the dye, the emission intensity increases significantly and can be detected by fluorescence spectroscopy or, similarly, by using a fluorescence plate reader. In sensing of arginase, posttranslational modification of the peptide sequence will occur, transforming arginine to ornithine. This changes the conformational dynamics of the oligopeptide tether, leading to the increase of the TCPP signal. This is a highly selective technology, which has a very low limit of detection (LOD) of 1 x 10⁻¹⁶ molL⁻¹ for proteases and arginase. The potential of this nanobiosensor technology to detect early pancreatic and lung cancer was demonstrated by using serum samples, which were collected from patients who have been diagnosed with pancreatic cancer and non-small cell lung cancer at the South Eastern Nebraska Cancer Center (lung cancer) and the University of Kansas Cancer Center (pancreatic cancer). As controls, serum samples collected from healthy volunteers were analyzed. In pancreatic cancer detection, the protease/arginase signature for the detection of pancreatic adenocarcinomas in serum was identified. It comprises arginase, MMPs -1, - 3, and -9, cathepsins -B and -E, urokinase plasminogen activator, and neutrophil elastase. For lung cancer detection, the specificity and sensitivity of the nanobiosensors permit the accurate measurements of the activities of nine signature proteases in serum samples. Cathepsin -L and MMPs-1, -3, and -7 permit detecting non-small-cell lung-cancer at stage 1.
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Al, Mohammad Badera. "Lung Cancer Detection on Chest Computed Tomography Scan: Observer Performance and the Effect of Cancer Nodules’ Characteristics." Thesis, The University of Sydney, 2018. http://hdl.handle.net/2123/20054.

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Анотація:
Aims: The aims are to measure radiologists’ performance in lung cancer detection using chest CT; compare the performance of radiology-residents to that of board-certified radiologists in lung cancer detection and to investigate the associations between lung cancer detectability and nodule characteristics including size, lobe location, surrounding-background and primary lung cancer cell type. Methods: 30 radiologists read 60 chest CT scans (30 lung cancer cases, 30 cancer-free). The readers were requested to identify and localize suspicious nodules and give each a confidence score from 1 to 5. The performance of different subgroups was compared using the two-sample t-test. Multiple logistic regression was used to estimate the association between the four characteristics and cancer detection sensitivity. Results: Radiologists’ performance had the following mean values: sensitivity=0.749, location sensitivity=0.666, specificity=0.81, AUC=0.846 and sensitivity at fixed specificity=0.744. In study two, readers had the following (radiologists, residents) pairs of values: sensitivity=(0.782, 0.687); location sensitivity=(0.702, 0.597); specificity=(0.8, 0.83); AUC=(0.844, 0.85) and sensitivity for fixed 0.8 specificity=(0.74, 0.73). In study three, the multivariable regression model demonstrated that the adjusted ORs for all four predictors were significant. Conclusion: Radiologists performance was similar to studies performed elsewhere. Senior-residents compared favorably with board-certified radiologists in the ability to discriminate between diseased and non-diseased patients; however they had significantly lower lung cancer detection sensitivity. Lung cancer features that contributed to reduced detection were: lower-lobe location, non-isolated nodules, NSCLC and smaller size nodules, with size having the largest effect on the predictive value.
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Rakhit, Callum Paul. "Circulating DNA for the in vivo detection and monitoring of lung cancer." Thesis, University of Leicester, 2018. http://hdl.handle.net/2381/42486.

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Анотація:
Lung cancer is the most lethal cancer globally, in part because it is often diagnosed at an incurable stage. Furthermore, limited options exist for effective patient treatment stratification within the disease. Circulating cell free (cf) DNA provides an accessible non-invasive source of patient tumour DNA, potentially useful for the detection of early stage lesions, or for identifying biomarkers predictive of response to treatment in more advanced disease. The molecular chaperone Heat Shock Protein 90 (HSP90) is a promising therapeutic target in cancer, though predictive biomarkers remain elusive. Using cfDNA it was possible to identify genetic alterations predictive of patient survival in GALAXY-1, a clinical trial designed to assess the efficacy of the HSP90 inhibitor ganetespib. It was also found that low cfDNA concentration was prognostic of improved survival, and a high somatic tumour burden predicted improved survival under the treatment arm containing ganetespib. Furthermore, using FFPE samples it was also possible to identify somatic copy number alterations (SCNAs) predictive of survival, and a positive correlation between the number of SCNAs found and worsened survival was observed. Experimental workflows established that peptide nucleic acid (PNA) clamps can improve the sensitivity of mutation detection in next generation sequencing (NGS) workflows using cfDNA. It was also demonstrated that NGS data that is routinely discarded, such as information on synonymous mutations that alter tRNA pools, can give potentially useful information on somatic alterations in a patient’s cancer. Reduced cfDNA integrity was found to be prognostic of worsened survival in the GALAXY-1 patients. To explore the hypothesis that analysis of cfDNA could allow for the identification of early stage preneoplastic lung cancer the genetically engineered KRAS+/LSL-G12D mouse model was utilised. cfDNA levels were found to correlate with tumour burden, with tumour bearing mice having significantly greater levels of cfDNA versus mice without premalignant lesions, and versus earlier samples taken from the same mice before the development of a tumour burden. It was also shown that the recombined KRAS allele could be detected in circulation. Additionally, CCR1 inhibition was demonstrated to have potential therapeutic benefit in the KRASLSL-G12D model, by causing significant alterations in the recruitment of hematopoietic cells to KRAS mutant murine lungs.
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Somers, Veerle Anne-Marie Christine. "The role of K-ras point mutation detection in lung cancer towards a strategy for early detection /." [Maastricht : Maastricht : Universiteit Maastricht] ; University Library, Maastricht University [Host], 1999. http://arno.unimaas.nl/show.cgi?fid=8568.

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Craig, Daniel John. "Low Frequency Airway Epithelial Cell Mutation Pattern Associated with Lung Cancer Risk." University of Toledo Health Science Campus / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=mco1556918218571742.

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Alves, Jeovane Honório. "A lung cancer detection approach based on shape index and curvedness superpixel candidate selection." reponame:Repositório Institucional da UFPR, 2016. http://hdl.handle.net/1884/45760.

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Анотація:
Orientador : Lucas Ferrari de Oliveira
Dissertação (mestrado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Engenharia Elétrica. Defesa: Curitiba, 29/08/2016
Inclui referências : f. 72-76
Área de concentração: Sistemas eletrônicos
Resumo: Câncer é uma das causas com mais mortalidade mundialmente. Câncer de pulmão é o tipo de câncer mais comum (excluíndo câncer de pele não-melanoma). Seus sintomas aparecem em estágios mais avançados, o que dificulta o seu tratamento. Para diagnosticar o paciente, a tomografia computadorizada é utilizada. Ela é composta de diversos cortes, que mapeiam uma região 3D de interesse. Apesar de fornecer muitos detalhes, por serem gerados vários cortes, a análise de exames de tomografia computadorizada se torna exaustiva, o que pode influenciar negativamente no diagnóstico feito pelo especialista. O objetivo deste trabalho é o desenvolvimento de métodos para a segmentação do pulmão e a detecção de nódulos em imagens de tomografia computadorizada do tórax. As imagens são segmentadas para separar o pulmão das outras estruturas e após, detecção de nódulos utilizando a técnicas de superpixeis são aplicadas. A técnica de Rótulamento dos Eixos teve uma média de preservação de nódulos de 93,53% e a técnica Monotone Chain Convex Hull apresentou melhores resultados com uma taxa de 97,78%. Para a detecção dos nódulos, as técnicas Felzenszwalb e SLIC são empregadas para o agrupamento de regiões de nódulos em superpixeis. Uma seleção de candidatos à nódulos baseada em shape index e curvedness é aplicada para redução do número de superpixeis. Para a classificação desses candidatos, foi utilizada a técnica de Florestas Aleatórias. A base de imagens utilizada foi a LIDC, que foi dividida em duas sub-bases: uma de desenvolvimento, composta pelos pacientes 0001 a 0600, e uma de validação, composta pelos pacientes 0601 a 1012. Na base de validação, a técnica Felzenszwalb obteve uma sensibilidade de 60,61% e 7,2 FP/exame. Palavras-chaves: Câncer de pulmão. Detecção de nódulos. Superpixel. Shape index.
Abstract: Cancer is one of the causes with more mortality worldwide. Lung cancer is the most common type (excluding non-melanoma skin cancer). Its symptoms appear mostly in advanced stages, which difficult its treatment. For patient diagnostic, computer tomography (CT) is used. CT is composed of many slices, which maps a 3D region of interest. Although it provides many details, its analysis is very exhaustive, which may has negatively influence in the specialist's diagnostic. The objective of this work is the development of lung segmentation and nodule detection methods in chest CT images. These images are segmented to separate the lung region from other parts and, after that, nodule detection using superpixel methods is applied. The Axes' Labeling had a mean of nodule preservation of 93.53% and the Monotone Chain Convex Hull method presented better results, with a mean of 97.78%. For nodule detection, the Felzenszwalb and SLIC methods are employed to group nodule regions. A nodule candidate selection based on shape index and curvedness is applied for superpixel reduction. Then, classification of these candidates is realized by the Random Forest. The LIDC database was divided into two data sets: a development data set composed of the CT scans of patients 0001 to 0600, and a untouched, validation data set, composed of patients 0601 to 1012. For the validation data set, the Felzenszwalb method had a sensitivity of 60.61% and 7.2 FP/scan. Key-words: Lung cancer. Nodule detection. Superpixel. Shape index.
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Senden, Nicole Hubertina Maria. "NSP-reticulons characterization and use for the detection of neuroendocrine differentiation in lung cancer /." Maastricht : Maastricht : Universitaire Pers Maastricht ; University Library, Maastricht University [Host], 1995. http://arno.unimaas.nl/show.cgi?fid=8353.

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TRAVERSO, ALBERTO. "Development and application in clinical practice of Computer-aided Diagnosis systems for the early detection of lung cancer." Doctoral thesis, Politecnico di Torino, 2017. http://hdl.handle.net/11583/2686725.

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Анотація:
Lung cancer is the main cause of cancer-related deaths both in Europe and United States, because often it is diagnosed at late stages of the disease, when the survival rate is very low if compared to first asymptomatic stage. Lung cancer screening using annual low-dose Computed Tomography (CT) reduces lung cancer 5-year mortality by about 20% in comparison to annual screening with chest radiography. However, the detection of pulmonary nodules in low-dose chest CT scans is a very difficult task for radiologists, because of the large number (300/500) of slices to be analyzed. In order to support radiologists, researchers have developed Computer aided Detection (CAD) algorithms for the automated detection of pulmonary nodules in chest CT scans. Despite proved benefits of those systems on the radiologists detection sensitivity, the usage of CADs in clinical practice has not spread yet. The main objective of this thesis is to investigate and tackle the issues underlying this inconsistency. In particular, in Chapter 2 we introduce M5L, a fully automated Web and Cloud-based CAD for the automated detection of pulmonary nodules in chest CT scans. This system introduces a new paradigm in clinical practice, by making available CAD systems without requiring to radiologists any additional software and hardware installation. The proposed solution provides an innovative cost-effective approach for clinical structures. In Chapter 3 we present our international challenge aiming at a large-scale validation of state-of-the-art CAD systems. We also investigate and prove how the combination of different CAD systems reaches performances much higher than any best stand-alone system developed so far. Our results open the possibility to introduce in clinical practice very high-performing CAD systems, which miss a tiny fraction of clinically relevant nodules. Finally, we tested the performance of M5L on clinical data-sets. In chapter 4 we present the results of its clinical validation, which prove the positive impact of CAD as second reader in the diagnosis of pulmonary metastases on oncological patients with extra-thoracic cancers. The proposed approaches have the potential to exploit at best the features of different algorithms, developed independently, for any possible clinical application, setting a collaborative environment for algorithm comparison, combination, clinical validation and, if all of the above were successful, clinical practice.
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Broyelle, Antoine. "Automated Pulmonary Nodule Detection on Computed Tomography Images with 3D Deep Convolutional Neural Network." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-231930.

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Анотація:
Object detection on natural images has become a single-stage end-to-end process thanks to recent breakthroughs on deep neural networks. By contrast, automated pulmonary nodule detection is usually a three steps method: lung segmentation, generation of nodule candidates and false positive reduction. This project tackles the nodule detection problem with a single stage modelusing a deep neural network. Pulmonary nodules have unique shapes and characteristics which are not present outside of the lungs. We expect the model to capture these characteristics and to only focus on elements inside the lungs when working on raw CT scans (without the segmentation). Nodules are small, distributed and infrequent. We show that a well trained deep neural network can spot relevantfeatures and keep a low number of region proposals without any extra preprocessing or post-processing. Due to the visual nature of the task, we designed a three-dimensional convolutional neural network with residual connections. It was inspired by the region proposal network of the Faster R-CNN detection framework. The evaluation is performed on the LUNA16 dataset. The final score is 0.826 which is the average sensitivity at 0.125, 0.25, 0.5, 1, 2, 4, and 8 false positives per scan. It can be considered as an average score compared to other submissions to the challenge. However, the solution described here was trained end-to-end and has fewer trainable parameters.
Objektdetektering i naturliga bilder har reducerates till en enstegs process tack vare genombrott i djupa neurala nätverk. Automatisk detektering av pulmonella nodulärer är vanligtvis ett trestegsproblem: segmentering av lunga, generering av nodulärkandidater och reducering av falska positiva utfall. Det här projektet tar sig an nodulärdetektering med en enstegsmodell med hjälp av ett djupt neuralt nätverk. Pulmonella nodulärer har unika karaktärsdrag som inte finns utanför lungorna. Modellen förväntas fånga dessa drag och enbart fokusera på element inuti lungorna när den arbetar med datortomografibilder. Nodulärer är små och glest föredelade. Vi visar att ett vältränat nätverk kan finna relevanta särdrag samt föreslå ett lågt antal intresseregioner utan extra för- eller efter- behandling. På grund av den visuella karaktären av det här problemet så designade vi ett tredimensionellt s.k. convolutional neural network med residualkopplingar. Projektet inspirerades av Faster R-CNN, ett nätverk som utmärker sig i sin förmåga att detektera intresseregioner. Nätverket utvärderades på ett dataset vid namn LUNA16. Det slutgiltiga nätverket testade 0.826, vilket är genomsnittlig sensitivitet vid 0.125, 0.25, 0.5, 1, 2, 4, och 8 falska positiva per utvärdering. Detta kan anses vara genomsnittligt jämfört med andra deltagande i tävlingen, men lösningen som föreslås här är en enstegslösning som utför detektering från början till slut och har färre träningsbara parametrar.
La détection d’objets sur les images naturelles est devenue au fil du temps un processus réalisé de bout en bout en une seule étape grâce aux évolutions récentes des architectures de neurones artificiels profonds. En revanche, la détection automatique de nodules pulmonaires est généralement un processus en trois étapes : la segmentation des poumons (pré-traitement), la génération de zones d’intérêt (modèle) et la réduction des faux positifs (post-traitement). Ce projet s’attaque à la détection des nodules pulmonaires en une seule étape avec un réseau profond de neurones artificiels. Les nodules pulmonaires ont des formes et des structures uniques qui ne sont pas présentes en dehors de cet organe. Nous nous attendons à ce qu’un modèle soit capable de capturer ces caractéristiques et de se focaliser uniquement sur les éléments à l’intérieur des poumons alors même qu’il reçoit des images brutes (sans segmentation des poumons). Les nodules sont petits, peu fréquents et répartis aléatoirement. Nous montrons qu’un modèle correctement entraîné peut repérer les éléments caractéristiques des nodules et générer peu de localisations sans pré-traitement ni post-traitement. Du fait de la nature visuelle de la tâche, nous avons développé un réseau neuronal convolutif tridimensionnel. L’architecture utilisée est inspirée du méta-algorithme de détection Faster R-CNN. L’évaluation est réalisée avec le jeu de données du challenge LUNA16. Le score final est de 0.826 qui représente la sensibilité moyenne pour les valeurs de 0.125, 0.25, 0.5, 1, 2, 4 et 8 faux positifs par scanner. Il peut être considéré comme un score moyen comparé aux autres contributions du challenge. Cependant, la solution décrite montre la faisabilité d’un modèle en une seule étape, entraîné de bout en bout. Le réseau comporte moins de paramètres que la majorité des solutions.
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Книги з теми "LUNG CANCER DETECTION"

1

Canadian Institute for Radiation Safety. CAIRS early lung cancer detection program: Report. Toronto, Ont: CARIS, Canadian Institute for Radiation Safety, 1989.

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Szamosi, Alfred. Lung cancer: The art of detection by conventional radiography. Stockholm, Sweden: The Foundation, 1995.

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Bunn, Paul A., Harubumi Kato, and James L. Textbook of Prevention and Detection of Early Lung Cancer. Edited by Fred R. Hirsch. Abingdon, UK: Taylor & Francis, 1988. http://dx.doi.org/10.4324/9780203324523.

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R, Hirsch Fred, and International Association for the Study of Lung Cancer., eds. International Association for the Study of Lung Cancer textbook of prevention and detection of early lung cancer. London: Taylor & Francis, 2006.

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5

Oksa, Panu. Asbestosis, its detection, and predictors of progression and cancer. Helsinki: Finnish Institute of Occupational Health, 1998.

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6

Kim, Ho-jung. array-CGH rŭl iyong han piso sepʻo pʻyeam ŭi chogi chaebal pʻyojija mit chindan mohyŏng kaebal =: Development of early-recurrence detection marker and diagnostic model using array-CGH in NSCLC. [Seoul]: Pogŏn Pokchibu, 2007.

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7

King, Stephen. Night Shift. New York, USA: Anchor Books, 2011.

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8

King, Stephen. Night Shift. London: New English Library, 1986.

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King, Stephen. Night shift. Oxford: ISIS Large Print, 1994.

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King, Stephen. Night shift. Thorndike, Me: G.K. Hall, 1994.

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Частини книг з теми "LUNG CANCER DETECTION"

1

Jen, Rachel, and Stephen Lam. "Detection and Treatment of Preneoplastic Lesions." In Lung Cancer, 129–43. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118468791.ch7.

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Kim, Edward S., and Fadlo R. Khuri. "Prevention and Early Detection of Lung Cancer." In Lung Cancer, 256–79. New York, NY: Springer New York, 2003. http://dx.doi.org/10.1007/0-387-22652-4_14.

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Brambilla, Elisabeth. "Early Lung Cancer Detection." In Clinical and Biological Basis of Lung Cancer Prevention, 39–56. Basel: Birkhäuser Basel, 1998. http://dx.doi.org/10.1007/978-3-0348-8924-7_4.

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Hassanein, Mohamed, Melinda C. Aldrich, Stephen A. Deppen, Karl E. Krueger, Eric L. Grogan, and Pierre P. Massion. "Early Detection of Lung Cancer." In Biomarkers in Cancer Screening and Early Detection, 163–84. Chichester, UK: John Wiley & Sons, Ltd, 2017. http://dx.doi.org/10.1002/9781118468869.ch14.

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Wojtowicz, Malgorzata, and Eva Szabo. "Commonalities in Lung Cancer and COPD." In Biomarkers in Cancer Screening and Early Detection, 185–96. Chichester, UK: John Wiley & Sons, Ltd, 2017. http://dx.doi.org/10.1002/9781118468869.ch15.

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Nakajima, Takahiro, and Kazuhiro Yasufuku. "Early Lung Cancer: Methods for Detection." In Interventions in Pulmonary Medicine, 245–56. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58036-4_14.

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Suzuki, Kenji. "Computer-Aided Detection of Lung Cancer." In Image-Based Computer-Assisted Radiation Therapy, 9–40. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-2945-5_2.

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Mahima, S., S. Kezia, and E. Grace Mary Kanaga. "Deep Learning-Based Lung Cancer Detection." In Lecture Notes in Electrical Engineering, 633–41. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2177-3_59.

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Saranya, N., N. Kanthimathi, P. Saravanakumar, M. Kiruthika, G. Kavitha, and R. Narthika. "Lung Cancer Detection Using SVM Classification." In Advances in Intelligent Systems and Computing, 715–28. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7330-6_53.

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Yasufuku, Kazuhiro. "Early Lung Cancer: Methods for Detection." In Interventions in Pulmonary Medicine, 211–19. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-6009-1_13.

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Тези доповідей конференцій з теми "LUNG CANCER DETECTION"

1

Monif, Mamdouh, Kinan Mansour, Waad Ammar, and Maan Ammar. "Automatic Detection and Extraction of Lungs Cancer Nodules Using Connected Components Labeling and Distance Measure Based Classification." In 11th International Conference on Computer Science and Information Technology (CCSIT 2021). AIRCC Publishing Corporation, 2021. http://dx.doi.org/10.5121/csit.2021.110705.

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Анотація:
We introduce in this paper a method for reliable automatic extraction of lung area from CT chest images with a wide variety of lungs image shapes by using Connected Components Labeling (CCL) technique with some morphological operations. The paper introduces also a method using the CCL technique with distance measure based classification for the efficient detection of lungs nodules from extracted lung area. We further tested our complete detection and extraction approach using a performance consistency check by applying it to lungs CT images of healthy persons (contain no nodules). The experimental results have shown that the performance of the method in all stages is high.
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2

Wang, Lulu, and Hu Peng. "A Feasibility Study of Lung Cancer Detection Using Holographic Microwave Imaging." In ASME 2017 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/imece2017-70062.

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This paper presents the feasibility of using holographic microwave imaging (HMI) method for diagnosing lung tumour. A numerical imaging system is developed to evaluate the working principle, which includes a realistic CT-based thorax model. Results show that various small lung tumours with arbitrary shapes, sizes and locations can be identified in the reconstructed images. The HMI approach has a potential for lung cancer detection.
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3

"Lung Cancer Prediction Using Machine Learning: A Systematic Review." In International Conference on Women Researchers in Electronics and Computing. AIJR Publisher, 2021. http://dx.doi.org/10.21467/proceedings.114.3.

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One of the large spread diseases in a human being is Lung Cancer. It remains a threat to society and is the cause of thousands of deaths worldwide. Early detection cause of lung cancer is an understandable perspective to maximize the opportunity of the existence of the patients. This paper is about the observation of lung cancer. Here, Computed Tomography (CT) is used for the observation of lung cancer. Various Algorithms are used to search out lung cancer prediction correctly like K Nearest Neighbor, SVM, Decision Tree, and many more. An Aim of the introduced analysis to design a model that can reduce the likelihood of lung cancer in a patient with maximum accuracy. We began by surveying various machine learning techniques, explaining a concise definition of the most normally used classification techniques for identifying lung cancer. Then, we analyze survey representable research works utilizing learning machine classification methods in this field. Moreover, an elaborated comparison table of surveyed paper is introduced.
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Vas, Moffy, and Amita Dessai. "Lung cancer detection system using lung CT image processing." In 2017 International Conference on Computing, Communication, Control and Automation (ICCUBEA). IEEE, 2017. http://dx.doi.org/10.1109/iccubea.2017.8463851.

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5

Gunaydin, Ozge, Melike Gunay, and Oznur Sengel. "Comparison of Lung Cancer Detection Algorithms." In 2019 Scientific Meeting on Electrical-Electronics & Biomedical Engineering and Computer Science (EBBT). IEEE, 2019. http://dx.doi.org/10.1109/ebbt.2019.8741826.

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6

Prakash, Varsha, and P. Smitha Vas. "Survey on Lung Cancer Detection Techniques." In 2020 International Conference on Computational Performance Evaluation (ComPE). IEEE, 2020. http://dx.doi.org/10.1109/compe49325.2020.9200019.

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Kurkure, Manasee, and Anuradha Thakare. "Lung cancer detection using Genetic approach." In 2016 International Conference on Computing Communication Control and automation (ICCUBEA). IEEE, 2016. http://dx.doi.org/10.1109/iccubea.2016.7860007.

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Ignatious, Sruthi, and Robin Joseph. "Computer aided lung cancer detection system." In 2015 Global Conference on Communication Technologies (GCCT). IEEE, 2015. http://dx.doi.org/10.1109/gcct.2015.7342723.

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9

Tejaswini, Chintakayala, P. Nagabushanam, Priyadharshini Rajasegaran, Palyam Rohith Johnson, and S. Radha. "CNN Architecture for Lung Cancer Detection." In 2022 IEEE 11th International Conference on Communication Systems and Network Technologies (CSNT). IEEE, 2022. http://dx.doi.org/10.1109/csnt54456.2022.9787650.

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S, Bharathy, Pavithra R, and Akshaya B. "Lung Cancer Detection using Machine Learning." In 2022 International Conference on Applied Artificial Intelligence and Computing (ICAAIC). IEEE, 2022. http://dx.doi.org/10.1109/icaaic53929.2022.9793061.

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Звіти організацій з теми "LUNG CANCER DETECTION"

1

Spira, Avrum E., and Emily Maple. Detection of Early Lung Cancer Among Military Personnel (DECAMP). Fort Belvoir, VA: Defense Technical Information Center, October 2012. http://dx.doi.org/10.21236/ada568356.

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Lois, Augusto. Early Lung Cancer Detection via Global Protein Modification Profiles. Fort Belvoir, VA: Defense Technical Information Center, December 2013. http://dx.doi.org/10.21236/ada600735.

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Wong, David. Salivary Proteomic and microRNA Biomarkers Development for Lung Cancer Detection. Fort Belvoir, VA: Defense Technical Information Center, August 2014. http://dx.doi.org/10.21236/ada613286.

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Wong, David. Salivary Proteomic and microRNA Biomarkers Development for Lung Cancer Detection. Fort Belvoir, VA: Defense Technical Information Center, August 2013. http://dx.doi.org/10.21236/ada593384.

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Dubinett, Steven M., Pierre Massion, Ignacio Wistuba, and Avrum Spira. Molecular Profiles for Lung Cancer Pathogenesis and Detection in U.S. Veterans. Fort Belvoir, VA: Defense Technical Information Center, December 2014. http://dx.doi.org/10.21236/ada612659.

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Wistuba, Ignacio I., and Humam Kadara. Molecular Profiles for Lung Cancer Pathogenesis and Detection in U.S. Veterans. Fort Belvoir, VA: Defense Technical Information Center, December 2014. http://dx.doi.org/10.21236/ada613782.

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Massion, Pierre, Steven M. Dubinett, Brigitte M. Gompers, Ignacio Wistuba, and Avrum Spria. Molecular Profiles for Lung Cancer Pathogenesis and Detection in U.S. Veterans. Fort Belvoir, VA: Defense Technical Information Center, December 2014. http://dx.doi.org/10.21236/ada614423.

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Saccomanno, G. Early Lung Cancer Detection in Uranium Miners with Abnormal Sputum Cytology. Office of Scientific and Technical Information (OSTI), June 2000. http://dx.doi.org/10.2172/834057.

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Saccomanno, G. Early lung cancer detection in uranium miners with abnormal sputum cytology. Office of Scientific and Technical Information (OSTI), August 1992. http://dx.doi.org/10.2172/7091811.

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Wistuba, Ignacio I., and Humam Kadara. Molecular Profiles for Lung Cancer Pathogenesis and Detection in U.S. Veterans. Fort Belvoir, VA: Defense Technical Information Center, October 2012. http://dx.doi.org/10.21236/ada574247.

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