Analytical Overview of Machine Learning Algorithms in Breast Cancer Screening: Clinical Workflow, Applications and Research Gaps

Breast cancer remains one of the leading causes of mortality among women worldwide.Early detection through screening is pivotal in improving survival rates and treatment outcomes.Over recent years, machine learning (ML) algorithms have emerged as powerful tools in enhancing breast cancer screening processes.This review paper provides a comprehensive analysis of the state-of-the-art ML algorithms employed in breast cancer screening.We explore various supervised, unsupervised, and reinforcement learning techniques, assessing their effectiveness in image analysis, risk prediction, and diagnostic accuracy.Key contributions include a detailed examination of convolutional neural networks (CNNs) in mammogram analysis, the role of support vector machines (SVMs) and random forests (RFs) in feature extraction and classification, and the application of ensemble methods in improving prediction robustness.Additionally, we discuss the integration of ML algorithms with clinical workflows, highlighting challenges such as data heterogeneity, interpretability, and ethical considerations.Through this analytical review, we aim to provide insights into the current landscape of ML applications in breast cancer screening, identify gaps in existing research, and suggest directions for future studies to enhance the efficacy and reliability of these technologies in clinical practice.