A Novel Model for Breast Cancer Detection and Classification

Abstract

Breast cancer is a dreadful disease that affects women globally. The occurrences of masses in the breast region are the main cause of breast cancer development. It is important to detect breast cancer as early as possible as this might increase the survival rate. The existing research methodologies have the problems of increased computation complexity and low detection accuracy. To overcome such problems, this paper proposes an efficient breast cancer detection and classification system based on mammogram images. Initially, the mammogram images are preprocessed so unwanted regions and noise are removed and the contrast of the images is enhanced using Homo Morphic Adaptive Histogram Equalization (HMAHE). Then, the breast boundaries are identified with the use of the canny edge detector. After that, the pectoral muscles present in the images are detected and removed using the Global Pixel Intensity-based Thresholding (GPIT) method. Then, the tumors are identified and segmented by the Centroid-based Region Growing Segmentation (CRGS) algorithm. Next, the tumors are segmented and clustered and feature extraction is carried out from the clustered tumors. After that, the necessary features are selected by using the Chaotic Function-based Black Widow Optimization Algorithm (CBWOA). The selected features are utilized by the Convolutional Squared Deviation Neural Network Classifier (CSDNN) which classifies the tumors into six different categories. The proposed model effectively detects and classifies breast tumors and its efficiency is experimentally proved by comparison with the existing techniques.