Classification and Detection of Breast Cancer Review

The Cancer is the second-leading cause of death for women aged 20–59 worldwide and very few men. Compared to other cancers, breast cancer kills more people. According to Cancer.org, 13% of American women are at risk of having this cancer, and approximately 80% of them advance, which decreases recovery and treatment success. Deep learning methods for BC detection have been successful thanks to AI. This improves early diagnosis, increasing patient survival. This publication provides a detailed review of the deep learning-based BC diagnosis literature. It aims to help practitioners and researchers understand this domain's issues and trends. This article reviews looks at deep learning methods for breast cancer detection. Next, we examine and synthesize the latest AI-based breast cancer diagnostic studies using multiple breast DL modalities. We also provide a complete overview of breast-cancer imaging datasets, emphasizing their importance in AI-driven algorithms and deep learning model training. The investigation found that the CNN is the most widely used and accurate BC diagnosis model. Also, accuracy measures are the key way to evaluate such models. To provide a complete reference for breast cancer imaging researchers from the details of the researchers works. we can say that The performance of breast cancer detection is influenced by three factors: (1) the efficacy of the CAD system, (2) the characteristics of the population under analysis, and (3) the proficiency of the radiologists utilizing the system. CAD can assist in detecting microcalcifications, which may serve as potential indicators of