Advancing Breast Cancer Identification: Exploring Deep Learning Models for Improved Detection

Abstract

Background: Breast cancer is the uncontrolled growth of abnormal cells within the breast tissue, and it can be either malignant or benign. Malignant tumors are cancerous and have the potential to invade and spread to other parts of the body while benign tumors are non-cancerous. Traditional breast cancer screening methods have limitations from artifacts and positioning errors. (2) Methods: This study employs various deep learning models-GELAN-C (Generalized Efficient Layer Aggregation Network - Compact version), YOLOv8 (You Only Look Once version 8), RTMDet (Real-Time Models for Object Detection), DETR (DEtection TRansformer), YOLO-NAS (You Only Look Once - Neural Architecture Search), and Detectron2-to identify the most effective and adaptable model for breast cancer diagnosis. (3) Results: From our experiment, GELAN- C outperformed other models providing exceptional accuracy with the highest mean average precision (mAP) scores of both thresholds. Specifically, GELAN-C achieved a mAP@0.50 of 0.983, which reflects overall object detection accuracy. Additionally, it maintained a strong mAP@0.50-0.95 of 0.868 indicating its robustness and precision across varying levels of detection difficulty.