Automated Breast Cancer Detection and Classification in Full Field Digital Mammograms Using Two Full and Cropped Detection Paths Approach

Breast cancer is one of the most severe diseases that threaten women's life results in increasing the death rate annually as confirmed by the World Health Organization. Breast cancer early detection is one of the main reasons behind reducing cancer severity. However, with the huge number of mammograms taken daily, the checking process conducted by radiologists becomes lengthy, tiring, and pruning to errors process. Hence, with the tremendous success achieved by utilizing CNNs in bioinformatics, the development of Computer-Aided Detection (CAD) systems has proved its necessity to solve the challenging cases for the biopsies missed by the ordinary checking leads to decreasing the false positive and negative rates. In this paper, we present a YOLOV4 based CAD system to localize lesions in full and cropped mammograms and then classify them to obtain their pathology type. The proposed method mainly consists of three phases that are applied on the full-field digital mammograms of the INbreast dataset. First, the mammograms are preprocessed to remove any extra artifacts and then cropped into small, overlapped slices. Second, masses are localized through two paths: the full mammograms and the cropped slices detection after configuring the YOLO-V4 model. Third, other feature extractors like ResNet, VGG, Inception, etc. are used to classify the localized lesions to compare their performance against YOLO. The proposed method proved using the experimental results the impact of utilizing YOLO-V4 as a detector with the 2-paths of detection of a full mammogram and the cropped slices in a trial to avoid any data loss by resizing the large-sized mammograms. Our system succeeds in detecting the masses' location with an overall accuracy of approximate to 98% which is more than the recently introduced breast cancer detection methods. Moreover, its ability to distinguish between benign and malignant tumors with an accuracy of approximate to 95%.