A Comparative Study of Machine Learning and Deep Learning Algorithms for the Detection and Classification of Breast Cancer From Mammography Images and Ultra Sound Images

Abstract

Based on uncontrolled cell division, cancer is an untreatable disease. The most common cancer in women globally is breast cancer, and early identification can reduce death rates. This paper emphasizes the significance of medical photographs in locating and diagnosing breast cancer, as they offer the most precise information. It explores the application of machine learning and deep learning techniques for breast cancer detection, specifically focusing on the classification of breast cancer using mammography images and ultrasound images. The classification systems for tumors, non-tumors and dense masses in a variety of medical imaging modalities are presented in detail. In the beginning, a range of study datasets are used to examine the differences between distinct medical image kinds. The diagnosis and classification of breast cancer can then be accomplished using a variety of machine learning and deep learning algorithms. The difficulties of classification and detection as well as the best outcomes of various strategies were covered in this review.