Meta Analysis Review on Machine Learning and Deep Learning Applications in Breast Cancer Detection

Many women worldwide are still affected by breast cancer which is known to be both widespread and dangerous. Finding it at an early stage is very important for saving more lives and making treatments more successful. Lately, the growth in AI, specifically in machine learning (ML) and deep learning (DL) technologies, has greatly affected the way medical diagnostics are carried out. They depend on computer-based models that can detect faint trends in broad and complicated groups of data which may include X-ray and ultrasound images, medical records and DNA codes. It brings together insights from 50 recent articles on ML and DL which discuss their role in detecting, classifying and predicting breast cancer. Review makes clear how AI studies are completed on various machine learning algorithms, using various medical databases and applied to different clinical needs. Studies have confirmed that deep learning, mainly convolutional neural networks (CNNs), are favored because they are made to learn from large multidimensional data. Nevertheless, support vector machines (SVMs), decision trees and ensemble methods are used widely, mainly in situations where processes or data follow typical structures. It also points out new research approaches, obstacles in data (like unequal numbers of samples) and an increasing interest in getting models to work on new, unseen data. Pulling together these insights, the meta-analysis wants to shape future breast cancer studies and help apply AI technology in patient care.