Detection Of Invasive Ductal Carcinoma In Histopathology Images Using Deep Learning

Abstract

These days, the most dangerous and fatal disease in women is breast cancer, which begins in the cells of the tissues. Due to its fatal consequences, most of the countries around the world especially the industrialized countries, are focusing in detection at early stage of breast cancer which can recover the long-term persistence rate of patients. Timely recognition is an operative way to succeed in curing breast cancer, which will provide improved chances of complete retrieval. However, the incongruity between increasing patients and the lack of practiced pathologists brings in a lot of challenges for the accurate identification of breast cancer. Modern medical image processing studies have proved promising results that may add to the analysis of biopsy breast cancer images by enhancing and revealing the possible unhealthy tissues during diagnosis. Computer aided detection (CAD) has proven as a supportive tool for breast cancer detection and classification by marking areas that are suspicious or doubtful in nature in the whole slide images, thus allowing the identification of cancer and reducing the rate of death among women with breast cancer. Computer technologies are used in these CAD systems to detect abnormalities in biopsy images automatically. newline