A Theoretical Methodology and Prototype Implementation for Detection Segmentation Classification of Digital Mammogram Tumor by Machine Learning and Problem Solving Approach
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.
Keywords: Digital Mammogram, Segmentation, Feature
Extraction and Classification
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