Breast cancer identification on digital mammogram using Evolving Connectionist Systems

This research aims to develop a system which can help radiologists to identify the symptoms of breast cancer, using Evolving Connectionist Systems (ECoS). Breast cancer identification is done by using Breast Imaging Reporting and Database System as a standard system. In this study, medical images will be enhanced by using computer image processing techniques. Then, the enhanced image will be classified, and the result will be given to the radiologist for further medical diagnosis. Region of Interest (ROI) Segmentation is applied in this system, followed by image enhancement using Contrast Limited Adaptive Histogram Equalization (CLAHE). For the extraction of textural features, Gray Level Co-occurrence matrix (GLCM)is applied. Then features parameter is employed to identify the ROIs as either masses or calcification and then classify them into three categories, they are normal, benign and malignant. Three layers Simple Evolving Connectionist Systems (SECoS) with sixteen features was proposed for classifying the marked regions into BI-RADS 2 (benign) or BI-RADS 5 (malignant). 75.00% sensitivity and 88.89% specificity is achieved on INbreast dataset. Wisconsin Breast Cancer dataset is also used in this paper, 96.20% sensitivity and 99.24% specificity is achieved.