Automated Breast Tumor Detection Using MRI Images

Abstract

Breast tumor is considered as one of the most familiar tumors among women which cause breast cancer. Breast abrasion is observed as a thickened block of cells which forms tumor cell. In this paper, an improved and efficient breast tumor detection approach has been delineated using MRI images which not only provides faster detection but also has better accuracy compared to other existing available works. Numerous abrasion regions which are not considered as breast tumor surrounded by actual breast tumor causes processing issues and hence analysis and identification becomes challenging. To overcome under or over segmentation issues associated with breast tumor local histogram processing was incorporated. Additionally, instead of using conventional filtering approaches in this work mathematical morphological operation was incorporated followed by identification using shape and size features. The approach used in this study indicates an accuracy of 96.41% for conventional method and 96.67% for machine learning based model (CNN). Both approaches have been accepted by the experts' in the histopathology laboratory.