Breast cancer continues to be a major cause of morbidity and mortality worldwide, particularly among women. Traditional methods of diagnosis, such as mammography and biopsy, though effective, often suffer from limitations including cost, accessibility, and subjectivity. The emergence of machine learning (ML) has opened new avenues in medical diagnostics by providing automated, efficient, and potentially more accurate systems. In this review, we explore recent ML advancements in breast cancer detection, emphasizing hybrid models that integrate various algorithms for improved diagnostic performance. Notably, the use of CNNs, LSTMs, and ensemble methods like Random Forests and Logistic Regression have shown significant promise. These techniques not only improve accuracy but also reduce human error and processing time. This paper outlines the system architectures employed, discusses proposed methodologies, and highlights the limitations of current systems. A proposed hybrid diagnostic model is detailed, combining spatial and temporal feature extraction with ensemble classification. We identify gaps in model interpretability, data quality, and clinical integration. Future research directions include explainable AI, robust data handling, and real-world deployment strategies. These methods have been tested on real-world clinical datasets with promising results. However, interpretability and model transparency remain challenges The paper concludes by affirming ML's pivotal role in modernizing breast cancer diagnostics. This review aims to provide a comprehensive understanding for researchers, clinicians, and developers interested in AI-driven healthcare solutions.
R et al. (Mon,) studied this question.