Machine-learning methods in detecting breast cancer and related therapeutic issues: a review

Abstract

ABSTRACT In 2020, the World Health Organization reported that breast cancer resulted in the deaths of 685,000 people worldwide, with 2.3 million women diagnosed with the disease. Breast cancer is the most common cancer globally, with 7.8 million women diagnosed in the past five years. Machine learning (ML) techniques can help identify breast cancer early and define its type by analyzing tumor size. ML models have been used for image classification and cancer prediction, and have been shown to be beneficial for breast cancer diagnosis. The current systematic review aims to highlight the gaps and shortcomings of previous works regarding the use of ML for breast cancer prediction based on image processing. The review updates publications to see the pros and cons of various ML and deep learning (DL) techniques, and can benefit medical practitioners seeking advanced therapies. The previous works mainly benefited from SVM, KNN, and DT in detecting BC; however, other techniques, especially the DL ones, can be useful.