Two-Stage approach for semantic image segmentation of breast cancer: deep learning and mass detection in mammographic images

Abstract

Breast cancer is a significant global health problem that predominantly affects women and requires effective screening methods. Mammography, the primary screening approach, presents challenges such as radiologist workload and associated costs. Recent advances in deep learning hold promise for improving breast cancer diagnosis. This paper focuses on early breast cancer detection using deep learning to assist radiologists, reduce their workload and costs. We employed the CBIS-DDSM dataset and various CNN models, including YOLO versions V5, V7, and V8 for mass detection, and transformer-based (nested) models inspired by ViT for mass segmentation. Our diverse approach aims to address the complexity of breast cancer detection and segmentation from medical images. Our results show promise, with a 59% mAP50 for cancer mass detection and an impressive 90.15% Dice coefficient for semantic segmentation. These findings highlight the potential of deep learning to enhance breast cancer diagnosis, paving the way for more efficient and accurate early detection methods.