9786139838868 - cad system for classification of breast cancer from mammogram images: a complete guide for research beginners de george m., jayesh; sankar s., perumal (8 resultados)
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Taschenbuch. Condición: Neu. CAD System for Classification of Breast Cancer from Mammogram Images | A complete guide for research beginners | Jayesh George M. (u. a.) | Taschenbuch | 80 S. | Englisch | 2018 | LAP LAMBERT Academic Publishing | EAN 9786139838868 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tar…pen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This Comprehensive text on CAD system for classification of breast cancer from mammogram images is designed for research scholars of biomedical image and signal processing. This book offers an introduction to Mammogram images and cl…assification of cancer from this images. Breast cancer is a leading cause of death among women and second main cause of death after lung cancer. In order to reduce unnecessary biopsies which cause patient anxiety and health care cost it is important to improve the accuracy of interpreting mammographic lesions thereby increasing the positive predictive value of mammography. Current CAD system is clinically used to serve as a second reader of breast cancer detection. Using the existing CAD, microcalcification can get better detection performance but detection performance for mass is not satisfactory. This work present a method to efficiently detect the mass abnormalities and classify the lesions as malignant or benign. The malignant lesions were further classified into types of malignant breast cancer. The proposed system uses hybrid image processing techniques for improving the accuracy of existing system. 80 pp. Englisch.
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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: George M. JayeshJayesh George M is Assistant Professor in Department of Electronics and Communication, Vimal Jyothi Engineering College, Kannur, Kerala. His area of specialization is Biomedical image an…d signal processing. He is a gr.
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Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This Comprehensive text on CAD system for classification of breast cancer from mammogram images is designed for research scholars of biomedical image and signal processing. This book offers an introduction to Mammogram images and classi…fication of cancer from this images. Breast cancer is a leading cause of death among women and second main cause of death after lung cancer. In order to reduce unnecessary biopsies which cause patient anxiety and health care cost it is important to improve the accuracy of interpreting mammographic lesions thereby increasing the positive predictive value of mammography. Current CAD system is clinically used to serve as a second reader of breast cancer detection. Using the existing CAD, microcalcification can get better detection performance but detection performance for mass is not satisfactory. This work present a method to efficiently detect the mass abnormalities and classify the lesions as malignant or benign. The malignant lesions were further classified into types of malignant breast cancer. The proposed system uses hybrid image processing techniques for improving the accuracy of existing system.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 80 pp. Englisch.
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Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This Comprehensive text on CAD system for classification of breast cancer from mammogram images is designed for research scholars of biomedical image and signal processing. This book offers an introduction to Mammogram images and classif…ication of cancer from this images. Breast cancer is a leading cause of death among women and second main cause of death after lung cancer. In order to reduce unnecessary biopsies which cause patient anxiety and health care cost it is important to improve the accuracy of interpreting mammographic lesions thereby increasing the positive predictive value of mammography. Current CAD system is clinically used to serve as a second reader of breast cancer detection. Using the existing CAD, microcalcification can get better detection performance but detection performance for mass is not satisfactory. This work present a method to efficiently detect the mass abnormalities and classify the lesions as malignant or benign. The malignant lesions were further classified into types of malignant breast cancer. The proposed system uses hybrid image processing techniques for improving the accuracy of existing system.
