A Theoretical Methodology and Prototype Implementation for Detection Segmentation Classification of Digital Mammogram Tumor by Machine Learning and Problem Solving Approach
Valliappan Raman, Putra Sumari, Rajeswari Mandava · Swinburne figshare (Swinburne University of Technology) · 2010
Breast cancer continues to be a significant public health problem in the world. Early detection is the key for improving breast cancer prognosis. The CAD systems can provide such help and they are important and necessary for breast cancer control. Microcalcifications and masses are the two most important indicators of malignancy, and their automated detection is very valuable for early breast cancer diagnosis. The main objective of this paper is to detect, segment and classify the tumor from mammogram images that helps to provide support for the clinical decision to perform biopsy of the breast. In this paper, a classification system for the analysis of mammographic tumor using machine learning techniques is presented. CBR uses a similar philosophy to that which humans sometimes use: it tries to solve new cases of a problem by using old previously solved cases. The paper focus on segmentation and classification by machine learning and problem solving approach, theoretical review have been undergone with more explanations. The paper also describes the theoretical methods of weighting the feature relevance in case base reasoning system.