Recent Advancements in Machine Learning Techniques for Breast Cancer Detection

Emerging critical illnesses pose substantial problems, including breast cancer, which can be efficiently treated if found early. Many instances of breast cancer are managed through early discovery, which reduces the death rate. There is a lot of technological innovation going on around the world. This technology can be used to diagnose diseases more accurately and in less time. Machine learning is one of the most utilized techniques in this field. This research aims to analyze and review numerous significant research articles released in recent times. This research has evaluated the available literature, classified the various Machine Learning approaches, and examined their performance measures. It also examines the used datasets, feature extraction methods, and assessment measures. It examines and discusses various recent machine learning (ML) and deep learning (DL) models developed for breast cancer detection. It evaluates these models using critical metrics such as accuracy, specificity, sensitivity, F-score, precision, and recall. Further, it explains the importance of these investigations, providing insights for future study prospects.