Academic literature on the topic 'Computer-aided lung cancer detection system'
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Journal articles on the topic "Computer-aided lung cancer detection system"
Ziyad, Shabana Rasheed, Venkatachalam Radha, and Thavavel Vayyapuri. "Overview of Computer Aided Detection and Computer Aided Diagnosis Systems for Lung Nodule Detection in Computed Tomography." Current Medical Imaging Formerly Current Medical Imaging Reviews 16, no. 1 (January 6, 2020): 16–26. http://dx.doi.org/10.2174/1573405615666190206153321.
Full textTaher, Fatma, Naoufel Werghi, and Hussain Al-Ahmad. "Computer Aided Diagnosis System for Early Lung Cancer Detection." Algorithms 8, no. 4 (November 20, 2015): 1088–110. http://dx.doi.org/10.3390/a8041088.
Full textAmin, Javeria, Muhammad Sharif, and Mussarat Yasmin. "Computer Aided Diagnosis Systems for Lung Cancer Detection." Immunology‚ Endocrine & Metabolic Agents in Medicinal Chemistry 16, no. 999 (October 14, 2016): 1. http://dx.doi.org/10.2174/1871522216666161014160132.
Full textEl-Baz, Ayman, Garth M. Beache, Georgy Gimel'farb, Kenji Suzuki, Kazunori Okada, Ahmed Elnakib, Ahmed Soliman, and Behnoush Abdollahi. "Computer-Aided Diagnosis Systems for Lung Cancer: Challenges and Methodologies." International Journal of Biomedical Imaging 2013 (2013): 1–46. http://dx.doi.org/10.1155/2013/942353.
Full textChapaliuk, Bohdan V., and Yuriy P. Zaychenko. "Recurrent neural network usage for computer-aided lung cancer detection system." System research and information technologies, no. 3 (October 7, 2019): 33–40. http://dx.doi.org/10.20535/srit.2308-8893.2019.3.03.
Full textLo, Pechin, Matthew S. Brown, Jonathan Goldin, Eran Barnoy, Hyun J. Kim, Michael F. McNitt-Gray, and Denise R. Aberle. "Computer-aided lung cancer screening with CT: A clinically usable nodule detection and assessment system." Journal of Clinical Oncology 31, no. 15_suppl (May 20, 2013): 7562. http://dx.doi.org/10.1200/jco.2013.31.15_suppl.7562.
Full textQu, Ming Zhi, and Gui Rong Weng. "Lung Nodule Segmentation Using Mathematical Morphology." Applied Mechanics and Materials 58-60 (June 2011): 1378–83. http://dx.doi.org/10.4028/www.scientific.net/amm.58-60.1378.
Full textWang, Hui, Yanying Li, Shanshan Liu, and Xianwen Yue. "Design Computer-Aided Diagnosis System Based on Chest CT Evaluation of Pulmonary Nodules." Computational and Mathematical Methods in Medicine 2022 (January 10, 2022): 1–12. http://dx.doi.org/10.1155/2022/7729524.
Full textR., Sudha, and Umamaheswari K.M. "A Comprehensive Study of Deep Learning Approaches for Lung Nodule Analysis with Recent Computational Techniques." Webology 19, no. 1 (January 20, 2022): 749–63. http://dx.doi.org/10.14704/web/v19i1/web19053.
Full textGomathi. "A Computer Aided Diagnosis System for Lung Cancer Detection \Using Support Vector Machine." American Journal of Applied Sciences 7, no. 12 (December 1, 2010): 1532–38. http://dx.doi.org/10.3844/ajassp.2010.1532.1538.
Full textDissertations / Theses on the topic "Computer-aided lung cancer detection system"
Чапалюк, Богдан Володимирович. "Системи автоматичної медичної комп’ютерної дiагностики з використанням методiв штучного iнтелекту." Doctoral thesis, Київ, 2020. https://ela.kpi.ua/handle/123456789/39677.
Full textΚορφιάτης, Παναγιώτης. "Ανίχνευση οζιδίων του πνεύμονα στην υπολογιστική αξονική τομογραφία χαμηλής δόσης." Thesis, 2006. http://nemertes.lis.upatras.gr/jspui/handle/10889/1167.
Full textΧρήση υπολογιστικής αξονικής τομογραφίας με πολλαπλών ανιχνευτών στον πληθυσμιακό έλεγχο καρκίνου το πνεύμονα αναμένεται να συμβάλει θετικά λόγω της ικανότητας της να ανιχνεύει οζίδια του πνεύμονα μικρού μεγέθους με αυξημένη ευαισθησία. Σε αυτή την μελέτη περιγράφεται η ανάπτυξη συστήματος αυτόματης ανίχνευσης οζιδίων του πνεύμονα, με στόχο την αύξηση της ευαισθησίας σε πολυτομική αξονική τομογραφία. Το σύστημα ανίχνευσης οζιδίων αποτελείται από τρία στάδια, το στάδιο της τμηματοποίησης των πνευμονικών πεδίων, την αναγνώριση των αρχικών υποψηφίων περιοχών και τέλος την μείωση των ψευδώς θετικών ενδείξεων. Η τμηματοποίηση των πνευμονικών πεδίων πραγματοποιήθηκε με τον συνδυασμό δύο αυτόματων τεχνικών τμηματοποίησης. Στην συνέχεια ένα επιλεκτικό ενισχυτικό φίλτρο εφαρμόζεται στην περιοχή των πνευμονικών πεδίων, για την ανίχνευση τον αρχικών υποψηφίων οζιδίων και τον συντεταγμένων τους. Τα όρια των υποψήφιων οζιδίων καθορίστηκαν με την βοήθεια ενός αλγορίθμου οριοθέτησης περιοχής με τις σταθερές κατωφλιού να υπολογίζονται αυτόματα βάση τις τεχνικής που προτάθηκε από τον Kittler et al. Η μείωση των ψευδώς θετικών ενδείξεων πραγματοποιήθηκε με την εφαρμογή δύο ταξινομητών Support Vector Machines (SVM) σε σειρά, οι οποίοι χρησιμοποίησαν 6 μορφολογικά χαρακτηριστικά τα οποία υπολογίστηκαν από τις περιοχές των υποψηφίων οζιδίων στην ενισχυμένη αλλά και στην αρχική εικόνα. Το σύστημα το οποίο παρουσιάζεται σε αυτή την εργασία εφαρμόστηκε και δοκιμάστηκε σε βάση δεδομένων αναφοράς η οποία περιλαμβάνει 21 εξετάσεις, την οποία τις παρέχει το Lung Imaging Database Consortium ((LIDC). Η απόδοση του συστήματος σε επίπεδο εξέτασης και επίπεδο τομής ήταν αντίστοιχα 91% και 81% με 5 ψευδώς θετικές ενδείξεις αντίστοιχα. Περαιτέρω ανάλυση βάση του μεγέθους, αντίθεσης και θέσης των οζιδίων απέδωσε ευαισθησίες 81%, 83% και 85% για οζίδια μικρού μεγέθους, χαμηλής αντίθεσης και οζίδια που βρίσκονται στον υπεζοκότα. Το προτεινόμενο σύστημα μπορεί να αποδειχθεί χρήσιμο εργαλείο υποβοήθησης ανάγνωσης οζιδίων σε πολυτομική αξονική τομογραφία για τους ακτινολόγους.
Chen, Yi-Wen, and 陳逸雯. "Computer-aided Diagnosis System for Lung Nodule Detection." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/72120452100264863240.
Full text中原大學
醫學工程研究所
90
In radiology, it''s quite difficult to make a preliminary diagnosis of lung cancer by using a chest X-ray image. When an uncertainly abnormal tissue occurred in X-ray image, some advanced tests, such as CT, MRI, bronchotomy , or microtomy , will be made for more detailed diagnosis. The aim of this study was to develop an algorithm applying in detecting lung nodules on chest radiological image, and mark possible region of suspicious nodules for doctor''s diagnosis. The detecting method was based on difference image. At first, we selected a threshold for all image and mark all suspicious nodules regions by circularity at difference image. Then, two algorithms were used on the suspicious regions for reducing the number of false positive(FP). A biological information which is determine whether calcification by gray scale and verify whether mediastinum by position was used to reduce the number of FP. If it didn''t show great effect, artificial neural network (ANN) was applied to reduce FP number. The source of our image contained phantom and clinical chest X-ray image. By using of phantom image , the correctness of our algorithm could be evaluated. The algorithm was applied on real clinical patient image to evaluate its clinically practical. Besides, by the aids of clinical doctors, we found that the difference of performance between our system and traditional method could be distinguished. Our system provided the calculation of suspicious area and successfully detect the nodules in phantom. In real image, the sensitivity approaches to 100% for less nodules patients when applied the gray level of 30% in cumulative histogram as a threshold. With the aids of biological information and ANN, the FP number went down from 24.92/per image to 3.06/per image. It satisfied the demand of medical diagnosis. For more nodules image, the sensitivity approached to 0.96667 when applied 32% gray level as a threshold. With the aids of biological information and ANN, the FP number west down from 17.33/per image to 2.00/per image. Comparing with other systems, this system obtained less FP and higher accuracy. Result showed that we can detect nodules in either phantom or real image successfully with less FP number. Besides, our system provides flexible user''s platform. In the future, some functions, such as area and calculation time ,will be improved so that the system can be more clinically practical and achieve the goal of assisting the diagnosis of doctor.
Chiang, Kuo-Hsien, and 江國賢. "Computer Aided Detection System of Vertebral Metastasis in Patients of Breast Cancer Using Computed Tomography Images." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/01752974014938908725.
Full text慈濟大學
醫學資訊學系碩士班
99
Bone metastases are commonly diagnosed in patients with advanced breast cancer, especially in vertebrae. Bone metastases can appear lytic, blastic, or anywhere in a continuum between these extremes. The presence or absence of bone metastases is a critical issue in the initial staging and follow-up of breast cancer because it can directly alter the therapeutic strategy. In this paper, we aim to developing a computer-aided diagnosis system for the detection of metastasis in vertebrae at whole body CT. We developed an automated method to extract ROIs of trabecular centrum from vertebrae. We computed 11 texture features and their inter-slice differences for each ROI. Total 33 features were fed into an MLP neural network to identify whether there is any abnormality in the trabecular centrum. The average accuracy, sensitivity, and specificity were 89.73%, 85.41% and 91.84%, respectively. The FN was substantially decreased from 20.83% to 14.58% when the inter-slice difference features were used.
Book chapters on the topic "Computer-aided lung cancer detection system"
Bhattacharjee, Ananya, and Swanirbhar Majumder. "Automated Computer-Aided Lung Cancer Detection System." In Advances in Communication, Devices and Networking, 425–33. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-3450-4_46.
Full textDesai, Usha, Sowmya Kamath, Akshaya D. Shetty, and M. Sandeep Prabhu. "Computer-Aided Detection for Early Detection of Lung Cancer Using CT Images." In Intelligent Sustainable Systems, 287–301. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2422-3_24.
Full textTiwari, Laxmikant, Rohit Raja, Vaibhav Sharma, and Rohit Miri. "Fuzzy Inference System for Efficient Lung Cancer Detection." In Computer Vision and Machine Intelligence in Medical Image Analysis, 33–41. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8798-2_4.
Full textNishio, Mizuho. "Computer-Aided Diagnosis of Lung Nodules: Systems for Estimation of Lung Cancer Probability and False-Positive Reduction of Lung Nodule Detection." In Lung Imaging and CADx, 107–32. Boca Raton : Taylor & Francis, 2018.: CRC Press, 2019. http://dx.doi.org/10.1201/9780429055959-6.
Full textNanglia, Pankaj, Sumit Kumar, Davinder Rathi, and Paramjit Singh. "Comparative Investigation of Different Feature Extraction Techniques for Lung Cancer Detection System." In Communications in Computer and Information Science, 296–307. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-3140-4_27.
Full textAlloghani, Mohamed, Thar Baker, Dhiya Al-Jumeily, Abir Hussain, Ahmed J. Aljaaf, and Jamila Mustafina. "Early Detection and Prediction of Lung Cancer using Machine-learning Algorithms Applied on a Secure Healthcare Data-system Architecture." In Machine Learning for Computer and Cyber Security, 233–57. Boca Raton, FL : Taylor & Francis Group, [2019] | “A science publishers book.”: CRC Press, 2019. http://dx.doi.org/10.1201/9780429504044-10.
Full textSuzuki, 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.
Full textYang, Zhou-nan, and Ji-qian Fang. "A Computer-Aided Diagnosis System for Lung Cancer." In Expert Systems and Decision Support in Medicine, 69–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-48706-4_10.
Full textCarrera, Enrique V., and David Ron-Domínguez. "A Computer Aided Diagnosis System for Skin Cancer Detection." In Communications in Computer and Information Science, 553–63. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-05532-5_42.
Full textZiyad, Shabana R., V. Radha, and V. Thavavel. "Performance Evaluation of Lung Segmentation Techniques in Computer Aided Lung Nodule Detection System." In Futuristic Trends in Networks and Computing Technologies, 619–33. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4451-4_49.
Full textConference papers on the topic "Computer-aided lung cancer detection system"
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.
Full textTaher, Fatma, Naoufel Werghi, and Hussain Al-Ahmad. "Computer aided diagnosis system for early lung cancer detection." In 2015 International Conference on Systems, Signals and Image Processing (IWSSIP). IEEE, 2015. http://dx.doi.org/10.1109/iwssip.2015.7313923.
Full textMahesh, Shanthi, Spoorthi Rakesh, and Vidya C. Patil. "Computer aided detection system for lung cancer using computer tomography scans." In INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONICS, MATERIALS AND APPLIED SCIENCE. Author(s), 2018. http://dx.doi.org/10.1063/1.5032063.
Full textPalma, David, Sarah Mattonen, Suresh Senan, Aaron D. Ward, and Salma Dammak. "Early detection of lung cancer recurrence after stereotactic ablative radiation therapy: radiomics system design." In Computer-Aided Diagnosis, edited by Kensaku Mori and Nicholas Petrick. SPIE, 2018. http://dx.doi.org/10.1117/12.2292444.
Full textAzuma, Yuta, Yoshiki Kawata, Noboru Niki, Issei Imoto, Masahiko Kusumoto, Yuji Matsumoto, Takaaki Tsuchida, and Masahiro Kaneko. "Early detection system using low-dose CT lung cancer screening and related SNP information." In Computer-Aided Diagnosis, edited by Karen Drukker and Maciej A. Mazurowski. SPIE, 2021. http://dx.doi.org/10.1117/12.2582092.
Full textDabade, Snehal, Shubhangi Chaudhari, Sneha Jadhav, and Arjun Nichal. "A review paper on computer aided system for lung cancer detection." In 2017 International Conference on Big Data, IoT and Data Science (BID). IEEE, 2017. http://dx.doi.org/10.1109/bid.2017.8336580.
Full textChapaliuk, Bohdan, and Yuriy Zaychenko. "Deep Learning Approach in Computer-Aided Detection System for Lung Cancer." In 2018 IEEE First International Conference on System Analysis & Intelligent Computing (SAIC). IEEE, 2018. http://dx.doi.org/10.1109/saic.2018.8516856.
Full textSuzuki, Hideo, Noriko Inaoka, Hirotsugu Takabatake, Masaki Mori, Soichi Sasaoka, Hiroshi Natori, and Akira Suzuki. "Development of a computer-aided detection system for lung cancer diagnosis." In Medical Imaging VI, edited by Murray H. Loew. SPIE, 1992. http://dx.doi.org/10.1117/12.59466.
Full textSekeroglu, Boran, and Erkan Emirzade. "A computer aided diagnosis system for lung cancer detection using support vector machine." In Third International Workshop on Pattern Recognition, edited by Xudong Jiang, Guojian Chen, and Zhenxiang Chen. SPIE, 2018. http://dx.doi.org/10.1117/12.2502010.
Full textKubota, Kazuhori, Mitsuru Kubo, Yoshiki Kawata, Noboru Niki, Kenji Eguchi, Hironobu Omatsu, Ryutaro Kakinuma, Masahiro Kaneko, and Noriyuki Moriyama. "Clinical test results of computer-aided detection system for lung cancer using helical CT images." In Medical Imaging 2001, edited by Milan Sonka and Kenneth M. Hanson. SPIE, 2001. http://dx.doi.org/10.1117/12.431083.
Full textReports on the topic "Computer-aided lung cancer detection system"
Chan, Heang P. Digital Mammography: Development of an Advanced Computer-Aided System for Breast Cancer Detection. Fort Belvoir, VA: Defense Technical Information Center, May 2004. http://dx.doi.org/10.21236/ada425978.
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