Critical Review on Deep Learning-Based Breast Cancer Detection and Segmentation: Challenges, Gaps, and Future Directions

Breast cancer remains a leading cause of cancer-related mortality among women worldwide, where early and accurate detection is vital for effective intervention and prognosis. Deep learning has emerged as a cornerstone in the automated analysis of breast cancer imaging, offering substantial improvements in tumor detection, segmentation, and classification across modalities such as mammography, ultrasound, and MRI. Despite notable progress, clinical integration remains constrained by challenges including limited dataset availability, suboptimal generalization, lack of interpretability, high computational complexity, and insufficient multi-task learning optimization. This critical review synthesizes findings from 70 peer-reviewed studies published between 2018 and 2025, encompassing convolutional neural networks, U-Net derivatives, Vision Transformers, instance segmentation models, and hybrid frameworks. Comparative evaluation highlights architectural strengths, modality-specific adaptations, and diagnostic performance metrics. Emphasis is placed on the comparative analysis of single-task versus multi-task frameworks, the integration of handcrafted features, transfer learning, and optimization strategies to improve model generalizability and robustness. Key limitations are identified in areas such as cross-domain robustness, real-time applicability, interpretability, and standardized benchmarking. Emerging solutions are examined, including self-supervised and semi-supervised learning strategies, lightweight and explainable architectures, adaptive loss balancing for MTL, cross-modal fusion techniques, and unified end-to-end pipelines.