Multi Scale Region based Advance Scheme for Breast Cancer Detection and Classification
Anilkumar C. Suthar · SSRN Electronic Journal · 2020
Breast cancer diagnosis is usually performed by doctors based on Digital Mammography (DM) or on Medical Images (MI). In order to assist doctors to process big amount of images for different patients, Breast Cancer Computer Aided Diagnosis (BC- CAD) is becoming, nowadays, an appealing area of research Using image processing techniques for computer-aided diagnosis that involves the feature extraction for cancer detection, so as to help doctors towards making optimal decisions quickly and accurately. Features play an important role in detecting the cancer in the digital mammogram and feature extraction stage is the most vital and difficult stage. In this research, an enhanced feature extraction method named Multi-scale Surrounding Region Method (MSRM) is proposed to be effective in classifying the mammogram images into normal or benign or malignant. This proposed system is based on a four-step procedure: Regions of Interest specification, segmentation base on edge and thresholding, and multi-scale surrounding region dependence matrix computation and feature extraction. After that we apply machine learning mechanism for classify breast cancer on early stage as soon as possible and we also use segmentation approach for that. after Implement Proposed Algorithm achieve more than 93% Accuracy for detect and Classify Breast Cancer with Benign and Malignant type tumors.