Advancements in Machine Learning and Deep Learning for Breast Cancer Detection: A Systematic Review

Abstract

Breast cancer is a significant transnational health concern, requiring effective timely detection methods to improve patient’s treatment result and reduce mortality rates. While conventional screening methods like mammography, ultrasound, and MRI have proven efficacy, they possess limitations, such as false-positive results and discomfort. In recent years, machine learning (ML) and deep learning (DL) techniques have demonstrated significant potential in transforming breast cancer detection through the analysis of imaging data. This review systematically explores recent advancements in the research of machine learning and deep learning applications for detecting breast cancer. Through a systematic analysis of existing literature, we identify trends, challenges, and opportunities in the development and deployment of ML and DL models for breast cancer screening and diagnosis. We highlight the crucial role of early detection in enhancing patient outcomes and lowering breast cancer mortality rates. Furthermore, we highlight the potential impact of ML and DL technologies on clinical procedure, patient outcomes, and healthcare delivery in breast cancer detection. By systematically identifying and evaluating studies on machine learning and deep learning applications in breast cancer detection, we aim to provide valuable insights for researchers, clinicians, policymakers, and healthcare stakeholders interested in leveraging advanced computational techniques to enhance breast cancer screening and diagnosis.