CAD System for Classification of Breast Cancer from Mammogram Images | A complete guide for research beginners

Jayesh George M. (u. a.)

ISBN 10: 613983886X / ISBN 13: 9786139838868

Published by LAP LAMBERT Academic Publishing, 2018

About this Item

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 Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu Print on Demand.

Seller Inventory # 114003010

Bibliographic Details

• Title: CAD System for Classification of Breast Cancer from Mammogram Images | A complete guide for research beginners
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2018
• Language: English
• ISBN 10: 613983886X
• ISBN 13: 9786139838868
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 5D millimeters 220 millimeters width by 150 millimeters height by 5 millimeters depth
• Weight: 137 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

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 classification 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.