Efficient Machine Learning and Deep Learning Techniques for Detection of Breast Cancer Tumor

Abstract

The detection of cancer tumors is an essential component that has important consequences for the speedy involvement of medical professionals and the enhancement of patient outcomes. This review paper presents a complete study of the current body of research and methodology, as well as an in-depth assessment of the use of machine learning (ML) and deep learning (DL) in the detection of cancer tumors. In addition, the article gives a full analysis of the approaches involved. Machine learning and deep learning, which effectively handle ambiguity in the identification of malignant tumors, provide an alternative method for dealing with the complexity of brain tissue. This method is offered by a combination of machine learning and deep learning. The first part of the review draws attention to the significance of making an accurate diagnosis of breast cancer, highlights the limits of traditional diagnostic methods, and investigates the cutting-edge area of medical imaging technology. After that, it investigates the fundamentals of ML and DL and how they might be used to deal with the challenges that are inherent in the interpretation of complicated imaging data. In addition, the paper explores the ways in which models enhance the processes of feature extraction, picture segmentation, and classification in breast tumor detection systems.