Abstract
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 ≈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 ≈95%.
Highlights
Cancer is one of the most genetic and dangerous diseases that threaten people’s life especially due to its late discovery
We used the Intersection Over Union (IOU) metric to evaluate how much the model is accurate in localizing tumors in breast mammograms as follows: Area of Overlap
2) CLASSIFICATION EVALUATION METRICS Each localized mass is passed through a specific classifier to output its malignancy type it is compared against the given ground truth to get one of four options
Summary
Cancer is one of the most genetic and dangerous diseases that threaten people’s life especially due to its late discovery. Cancer has more than 100 kinds and one of the most common types of cancer is breast cancer. According to World Health Organization (WHO), breast cancer is considered the most common and dangerous cancer type that spreads all over the world and annually leads to a larger death rate besides lung. In the united states only, it is expected by WHO that in 2021 about 43,600 women will die from breast cancer. Each cancer type is mainly represented in either benign or malignant tumors [1]. Once the breast cancer appears in a specific part of the breast, it starts in invading the surrounding tissues nearby the suspected one very fast especially the malignant tumors. The benign tumors are sometimes greater than the malignant ones they are not spread widely like the malignant ones
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