Relevant bibliographies by topics / Computer-aided Medical Diagnosis System

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Computer-aided Medical Diagnosis System'

Author: Grafiati
Published: 5 June 2025
Last updated: 15 July 2025

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Journal articles on the topic "Computer-aided Medical Diagnosis System"

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Innocent, P. R., and R. I. John. "Computer aided fuzzy medical diagnosis." Information Sciences 162, no. 2 (2004): 81–104. http://dx.doi.org/10.1016/j.ins.2004.03.003.

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De Silva, N. T., and D. J. Jayamanne. "Computer-Aided Medical Diagnosis Using Bayesian Classifier - Decision Support System for Medical Diagnosis." International Journal of Multidisciplinary Studies 3, no. 2 (2017): 91. http://dx.doi.org/10.4038/ijms.v3i2.11.

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Mujalled, Hattan Omar, and Yasser Kadah. "Diagnosis of Diabetic Retinopathy Utilizing Computer-Aided Diagnosis System." American Journal of Computing and Engineering 3, no. 1 (2020): 1–23. http://dx.doi.org/10.47672/ajce.580.

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Purpose: Diabetes is considered one of most diseases spread among people; blindness is considered the most resulted effect. Diabetes can damage the retinal blood vessels and cause severe problems to the eyes, which may end with sight loss. Such medical condition is known as "diabetic retinopathy" (DR). In such a diagnosis, the retinal microvascular go through several stages of change threat. In the early stages of the DR, detecting the formation that happened to the retinal blood vessels helps prevent the disease's dangerous effects. Therefore, producing a method to diagnose the disease in the
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AKINWOLE, Agnes Kikelomo, Nureni Asafe YEKINI, Olamide Adetokunbo OLOYEDE, and Olanrewaju OJO. "Computer-Aided Medical Diagnosis System Using Logistics Regression Algorithms (LRA) Supervised Learning Approach." Engineering and Technology Journal 06, no. 12 (2021): 1076–83. https://doi.org/10.47191/etj/v6i12.02.

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This work focused on the designing of medical diagnosis system using Supervised Machine Learning. Logistics Regression Algorithms (LRA) was adopted, the label inputs for the data set which the symptoms were trained and mapped with the input of the user. Diagnosis of malaria was considered in this work; the system verified the value of the logical regression in the medical decision support system. Medical practitioners and other health workers can use this system to make better decisions in medical diagnosis for malaria. Adoption of this system will reduce stress of diagnoses malaria from patie
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Shin, Seung Won, and Kwang Gi Kim. "Role of Computer Aided Diagnosis (CAD) in Medical Imaging." Central Asian Journal of Medical Sciences 1, no. 1 (2015): 5–15. http://dx.doi.org/10.24079/cajms.2015.01.002.

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Objectives: We introduce the various methods of image analysis used for CAD. In addition, we provide a guide for the clinician through examples which use the CAD. Methods: Medical images consist of individual pixel elements, to which discrete brightness or color values are assigned. Various methods have been utilized for the analysis of these images. We introduce the five methods: shape analysis, texture analysis, parametric mapping analysis, classification methods and segmentation methods. Results: Various image analysis techniques used in the CAD are quantified by analyzing the various featu
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Park, M., B. Kang, S. J. Jin, and S. Luo. "Computer aided diagnosis system of medical images using incremental learning method." Expert Systems with Applications 36, no. 3 (2009): 7242–51. http://dx.doi.org/10.1016/j.eswa.2008.09.058.

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Tuersun, ALimujiang, Weiyang Fang, Chaomin Chen, and Wei Wang. "Deep Learning Applied to Medical Image Aided Diagnosis Systems." Journal of Big Data and Computing 2, no. 1 (2024): 36–43. http://dx.doi.org/10.62517/jbdc.202401105.

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At present, in the field of diagnostic medical imaging, only relying on doctors for diagnosis can no longer meet the needs of contemporary clinical development. Traditional computer-aided diagnostic systems are limited by their recognition ability and universality, and can only provide diagnostic decision-making references for doctors. With the in-depth application of artificial intelligence in the field of medical imaging, the application of deep learning technology in medical imaging-aided diagnosis systems, based on deep neural networks, can not only greatly reduce the workload of doctors,
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HAGIWARA, YUKI, VIDYA K. SUDARSHAN, SOOK SAM LEONG, ANUSHYA VIJAYNANTHAN, and KWAN HOONG NG. "APPLICATION OF ENTROPIES FOR AUTOMATED DIAGNOSIS OF ABNORMALITIES IN ULTRASOUND IMAGES: A REVIEW." Journal of Mechanics in Medicine and Biology 17, no. 07 (2017): 1740012. http://dx.doi.org/10.1142/s0219519417400127.

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Automation of diagnosis process in medical imaging using various computer-aided techniques is a leading topic of research. Among many computer-aided methods, nonlinear entropies are widely applied in the development of automated algorithms to diagnose abnormalities present in medical images. The use of entropy features in development of Computer-Aided Diagnosis (CAD) may enhance the accuracy of the system. Entropy features depict the nonlinearity of images and thereby the presence of complexity in the images. Various types of entropies have been employed in medical image analysis for automated
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Aziz, Miran Hakim, and Alan Anwer Abdulla. "Computer-Aided Diagnosis for the Early Breast Cancer Detection." UHD Journal of Science and Technology 7, no. 1 (2023): 7–14. http://dx.doi.org/10.21928/uhdjst.v7n1y2023.pp7-14.

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Computer-aided diagnosis (CAD) system is a prominent tool for the detection of different forms of diseases, especially cancers, based on medical imaging. Digital image processing is a critical in the processing and analysis of medical images for the disease diagnosis and detection. This study introduces a CAD system for detecting breast cancer. Once the breast region is segmented from the mammograms image, certain texture and statistical features are extracted. GLRLM feature extraction technique is implemented to extracted texture features. On the other hand, statistical features such as skewn
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V M, Nisha, and L. Jeganathan. "A symmetry based anomaly detection in brain using cellular automata for computer aided diagnosis." Indonesian Journal of Electrical Engineering and Computer Science 14, no. 1 (2018): 471. http://dx.doi.org/10.11591/ijeecs.v14.i1.pp471-477.

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Computer aided diagnosis (CAD) is an advancing technology in medical imaging. CAD acts as an additional computing power for doctors to interpret the medical images which leads to a more accurate diagnosis of the disease.CAD system increases the chances of detection of brain lesions by assisting the physicians in decreasing the observational oversight in the early stage of diseases.This paper focuses on the development of a cellular automata based model to find the anomaly prone areas in human brains.Because of the bilateral symmetric nature of human brain, a symmetry based cellular automata mo
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Dissertations / Theses on the topic "Computer-aided Medical Diagnosis System"

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Thandani, Samir Ram. "A software-based ultrasound system for medical diagnosis." Thesis, Massachusetts Institute of Technology, 1997. http://hdl.handle.net/1721.1/43496.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1997.<br>Includes bibliographical references (p. 113-115).<br>by Samir Ram Thadani.<br>M.Eng.
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Wu, Bangxian, and 吴邦限. "Clinical applications of imaging informatics: computer aided diagnosis of nasopharyngeal carcinoma based on PET-CTand multimedia electronic patient record system for neurosurgery." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2012. http://hub.hku.hk/bib/B48521917.

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Medical imaging informatics is one of the important research areas in radiology that studies how information available on medical images is retrieved, analyzed, and enhanced. Recent development in medical imaging informatics has resulted in improvement of diagnostic accuracy based on imaging examinations, as well as efficiency in clinical workflow. Computer aided diagnosis (CAD) and electronic patient record system (ePR) are both topics in medical imaging informatics that have matured from research concepts into commercially available computerized systems in clinical environment. The current
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Todd, Bryan S. "A formal approach to the design of medical diagnostic programs." Thesis, University of Oxford, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.670360.

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Elieson, S. Willard (Sanfred Willard). "Development of an Expert System to Teach Diagnostic Skills." Thesis, University of North Texas, 1990. https://digital.library.unt.edu/ark:/67531/metadc331448/.

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The primary purpose of the study was to develop an expert system that could C D perform medical diagnoses In selected problem areas, and C2) provide diagnostic Insights to assist medical students In their training. An expert system Is a computer-based set of procedures and algorithms that can solve problems In a given domain. Two research questions were proposed. The first was "Given a problem space defined by a matrix of diseases and symptoms, can a computer-based model be derived that will consistently perform accurate and efficient diagnoses of cases within that problem area?" The second qu
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Nie, Yali. "Automatic Melanoma Diagnosis in Dermoscopic Imaging Base on Deep Learning System." Licentiate thesis, Mittuniversitetet, Institutionen för elektronikkonstruktion, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-41751.

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Melanoma is one of the deadliest forms of cancer. Unfortunately, its incidence rates have been increasing all over the world. One of the techniques used by dermatologists to diagnose melanomas is an imaging modality called dermoscopy. The skin lesion is inspected using a magnification device and a light source. This technique makes it possible for the dermatologist to observe subcutaneous structures that would be invisible otherwise. However, the use of dermoscopy is not straightforward, requiring years of practice. Moreover, the diagnosis is many times subjective and challenging to reproduce.
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Ford, Ralph M. (Ralph Michael) 1965. "Computer-aided analysis of medical infrared images." Thesis, The University of Arizona, 1989. http://hdl.handle.net/10150/276986.

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Thermography is a useful tool for analyzing spinal nerve root irritation, but interpretation of digital infrared images is often qualitative and subjective. A new quantitative, computer-aided method for analyzing thermograms, utilizing the human dermatome map, is presented. Image processing and pattern recognition principles needed to accomplish this goal are discussed. Algorithms for segmentation, boundary detection and interpretation of thermograms are presented. An interactive, user-friendly program to perform this analysis has been developed. Due to the relatively large number of images in
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MAZZEO, JOHN, and JOHN MAZZEO. "AN EVALUATION OF THE LEVEL OF SKILL REQUIRED OF OPERATORS OF A COMPUTER-ASSISTED RADIOLOGIC TOTAL LUNG CAPACITY MEASUREMENT SYSTEM (RELIABILITY, VALIDITY)." Diss., The University of Arizona, 1985. http://hdl.handle.net/10150/188124.

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This research was conducted to obtain information regarding the feasibility of using non-medical personnel to obtain measurements of radiologic total lung capacity (TLC). Operators from each of four groups (general undergraduates, nursing students, medical students, radiologists) differing in the amount of medical training and/or experience reading x-rays, performed each of two tasks. The first task was the measurement of radiologic TLC for a set of twenty x-rays. The second task consisted of tracing the outline of the anatomical structures that must be identified in the execution of the radio
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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
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Kan, Pei Chen. "Computer aided design of home medical alert system." Connect to resource, 2008. http://hdl.handle.net/1811/35644.

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Oda, Masahiro, Takayuki Kitasaka, Kensaku Mori, and Yasuhito Suenaga. "DEVELOPMENT OF A COMPUTER AIDED DIAGNOSIS SYSTEM FOR COLORECTAL CANCER BASED ON NAVIGATION DIAGNOSIS." INTELLIGENT MEDIA INTEGRATION NAGOYA UNIVERSITY / COE, 2006. http://hdl.handle.net/2237/10473.

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Books on the topic "Computer-aided Medical Diagnosis System"

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Ginneken, Bram van, and Carol L. Novak. Medical imaging 2012: Computer-aided diagnosis : 7-9 February 2012, San Diego, California, United States. SPIE, 2012.

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CAD Conference (2008 San Diego, Calif.). Medical imaging 2008, computer-aided diagnosis: 19-21 February 2008, San Diego, California, USA. Edited by Giger Maryellen Lissak 1956-, Karssemeijer Nico, SPIE (Society), and American Association of Physicists in Medicine. SPIE, 2008.

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Summers, Ronald Marc, and Bram van Ginneken. Medical imaging 2011: Computer-aided diagnosis : 15-17 February 2011, Lake Buena Vista, United States. Edited by SPIE (Society), Dynasil Corporation RMD Research, and American Physiological Society (1887- ). SPIE, 2011.

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Giger, Maryellen Lissak, and Nico Karssemeijer. Medical imaging 2009: Computer-aided diagnosis : 10-12 February 2009, Lake Buena Vista, Florida, United States. Edited by SPIE (Society) and American Association of Physicists in Medicine. SPIE, 2009.

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van, Bemmel Jan H., Grémy François, Zvárová Jana, and International Medical Informatics Association, eds. Medical decision making: Diagnostic strategies and expert systems : proceedings of the IFIP-IMIA International Working Conference on Computer-Aided Medical Decision-Making, Prague, Czechoslovakia, 30 September-4 October 1985. North-Holland, 1985.

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

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Samanwoy, Ghosh-Dastidar, ed. Automated EEG-based diagnosis of neurological disorders: Inventing the future of neurology. CRC Press/Taylor & Francis, 2010.

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

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

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

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Book chapters on the topic "Computer-aided Medical Diagnosis System"

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Elseid, Arwa Ahmed Gasm, and Alnazier Osman Mohammed Hamza. "Medical and Mathematical Background." In Computer-Aided Glaucoma Diagnosis System. CRC Press, 2020. http://dx.doi.org/10.1201/9780367406288-2.

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Anter, Ahmed M., and Aboul Ella Hassenian. "Computer Aided Diagnosis System for Mammogram Abnormality." In Medical Imaging in Clinical Applications. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-33793-7_8.

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Vijaya, G. "Deep Learning-Based Computer-Aided Diagnosis System." In Application of Deep Learning Methods in Healthcare and Medical Science. Apple Academic Press, 2022. http://dx.doi.org/10.1201/9781003303855-2.

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Sreenarayanan, N. M., C. M. Deepa, K. P. Arjun, and Neethu Narayanan. "Deep Learning for Computer-Aided Medical Diagnosis." In Machine Learning and Analytics in Healthcare Systems. CRC Press, 2021. http://dx.doi.org/10.1201/9781003185246-4.

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Gao, Mei-Jing, Zhu Liu, Liu-Zhu Wang, Bo-Zhi Zhang, and Shi-Yu Li. "Optical Micro-scanning Reconstruction Technique for a Thermal Microscope Imaging System." In Medical Imaging and Computer-Aided Diagnosis. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5199-4_13.

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Hernández-Julio, Y. F., L. A. Díaz-Pertuz, M. Prieto-Guevara, et al. "Intelligent Fuzzy Clinical Decision Support System to Classify Breast Cancer—Case Study: The Wisconsin Dataset." In Medical Imaging and Computer-Aided Diagnosis. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-16-6775-6_44.

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Duarte, Ana, and Orlando Belo. "Learning from Failure: A Methodology for the Retrieve Stage of a Cardiovascular Case-Based Reasoning System." In Medical Imaging and Computer-Aided Diagnosis. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-16-6775-6_33.

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Reda, Islam, Ahmed Shalaby, Mohammed Elmogy, et al. "Image-Based Computer-Aided Diagnostic System for Early Diagnosis of Prostate Cancer." In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46720-7_71.

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Huang, Wei, Huiqi Li, Kap Luk Chan, Joo Hwee Lim, Jiang Liu, and Tien Yin Wong. "A Computer-Aided Diagnosis System of Nuclear Cataract via Ranking." In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04271-3_97.

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Mühlenbrock, Andre, René Weller, and Gabriel Zachmann. "Optimizing the Illumination of a Surgical Site in New Autonomous Module-based Surgical Lighting Systems." In Medical Imaging and Computer-Aided Diagnosis. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-16-6775-6_24.

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Conference papers on the topic "Computer-aided Medical Diagnosis System"

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Yuan, Jiajie, Linxiao Wu, Yulu Gong, Zhou Yu, Ziang Liu, and Shuyao He. "Research on Intelligent Aided Diagnosis System of Medical Image Based on Computer Deep Learning." In 2024 IEEE 2nd International Conference on Image Processing and Computer Applications (ICIPCA). IEEE, 2024. http://dx.doi.org/10.1109/icipca61593.2024.10709115.

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Gerogiannis, Demetris, Anastasios Arsenos, Dimitrios Kollias, Dimitris Nikitopoulos, and Stefanos Kollias. "Covid-19 Computer-Aided Diagnosis through AI-Assisted CT Imaging Analysis: Deploying a Medical AI System." In 2024 IEEE International Symposium on Biomedical Imaging (ISBI). IEEE, 2024. http://dx.doi.org/10.1109/isbi56570.2024.10635484.

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Bezerra, Vinícius Marques, Eutino Junior Vieira Sirqueira, Felipe Gonçalves Dos Santos, Carlos Estevão Bastos Sousa, and Jurandir Cavalcante Lacerda Júnior. "A Machine Learning-Based System for Medical Assistance in the Diagnosis of Cardiovascular Diseases." In 2024 L Latin American Computer Conference (CLEI). IEEE, 2024. http://dx.doi.org/10.1109/clei64178.2024.10700064.

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Zi, Yun, Xiaohan Cheng, Taiyuan Mei, Qi Wang, Zijun Gao, and Haowei Yang. "Research on Intelligent System of Medical Image Recognition and Disease Diagnosis Based on Big Data." In 2024 IEEE 2nd International Conference on Image Processing and Computer Applications (ICIPCA). IEEE, 2024. http://dx.doi.org/10.1109/icipca61593.2024.10709204.

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Suzuki, Hideo, Noriko Inaoka, Hirotsugu Takabatake, Masaki Mori, and Hiroshi Natori. "Computer-aided diagnosis system for lung tumors." In Medical Imaging 1996, edited by Murray H. Loew and Kenneth M. Hanson. SPIE, 1996. http://dx.doi.org/10.1117/12.237912.

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Caban, Jesus J., Jianhua Yao, Nilo A. Avila, Joseph R. Fontana, and Vincent C. Manganiello. "Texture-based computer-aided diagnosis system for lung fibrosis." In Medical Imaging, edited by Maryellen L. Giger and Nico Karssemeijer. SPIE, 2007. http://dx.doi.org/10.1117/12.709831.

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Su, Mingjian, Xuejun Zhang, Brent Liu, Kening Su, and Ryan Louie. "A handheld computer-aided diagnosis system and simulated analysis." In SPIE Medical Imaging, edited by Jianguo Zhang and Tessa S. Cook. SPIE, 2016. http://dx.doi.org/10.1117/12.2216421.

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Glocker, Ben, Sonja Buhmann, Chlodwig Kirchhoff, Thomas Mussack, Maximilian Reiser, and Nassir Navab. "Towards a computer-aided diagnosis system for colon motility dysfunctions." In Medical Imaging, edited by Maryellen L. Giger and Nico Karssemeijer. SPIE, 2007. http://dx.doi.org/10.1117/12.708900.

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Pedowski, Tomasz, Piotr Wasiewicz, Ryszard Maciejewski, and Grzegorz Wallner. "Proper medical diagnosis and treatment with computer aided system." In Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2009, edited by Ryszard S. Romaniuk and Krzysztof S. Kulpa. SPIE, 2009. http://dx.doi.org/10.1117/12.838242.

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Satoh, Hitoshi, Noboru Niki, Kenji Eguchi, et al. "Computer-aided diagnosis workstation and network system for chest diagnosis based on multislice CT images." In Medical Imaging, edited by Katherine P. Andriole and Khan M. Siddiqui. SPIE, 2008. http://dx.doi.org/10.1117/12.770770.

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Reports on the topic "Computer-aided Medical Diagnosis System"

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NOEL, WILLIAM P. Computer aided differential diagnosis in emergency situations (CADDIES) system. Office of Scientific and Technical Information (OSTI), 2000. http://dx.doi.org/10.2172/752056.

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Wei, Jun. Development of a Computer-Aided Diagnosis System for Early Detection of Masses Using Retrospectively Detected Cancers on Prior Mammograms. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada438612.

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Wei, Jun, Wendy M. Kohrt, L. M. Glode, Robert S. Schwartz, and Daniel W. Barry. Development of a Computer-aided Diagnosis System for Early Detection of Masses Using Retrospectively Detected Cancers on Prior Mammograms. Defense Technical Information Center, 2009. http://dx.doi.org/10.21236/ada510061.

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Wei, Jun. Development of a Computer-Aided Diagnosis System for Early Detection of Masses Using Retrospectively Detected Cancers on Prior Mammograms. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada458398.

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Wei, Jun. Development of a Computer-Aided Diagnosis System for Early Detection of Masses Using Retrospectively Detected Cancers on Prior Mammograms. Defense Technical Information Center, 2007. http://dx.doi.org/10.21236/ada472884.

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