Researchers have been motivated to create effective, dependable, and scalable computer-aided diagnostic (CAD) systems given the rising incidence of breast cancer and its high death rates. In contrast to conventional evaluations that are subject to human mistake, CAD systems that use AI and visual computing can offer more precise diagnoses. But maintaining resilience in the face of complicated inputs is still difficult. Deep learning methods are the most effective for identifying and categorizing breast cancer, while there is still room for improvement in their generalizability. This study looks at state-of-the-art deep learning algorithms for breast cancer diagnosis, such as novel CNN-based segmentation, classification, and detection techniques. It highlights the advantages and disadvantages of improved deep networks, such as RNNs and transfer learning networks. Unlike traditional models, segmented region-of-interest (ROI) features can improve efficiency by addressing feature-level class imbalance. Hybrid deep models, designed to overcome issues like lack of contextual features and gradient vanishing, retain optimal feature sets for learning and prediction, resulting in highly precise and applicable findings for breast cancer diagnosis. Combining hybrid deep features with spatio-textural features yields even better results. These insights can guide future innovations in CAD solutions, ensuring higher accuracy and early detection while handling large data volumes consistently.
Chintakunta et al. (2025) studied this question.