Diagnosis of digital mammograms using computer-assisted system: A Review

In recent decades, breast cancer has been consid ered the leading cause of death in Women. Therefore, raising awareness about early detection and diagnosis of breast cancer is extremely important to prevent severity of breast cancer and to improve the quality of life of patients. Digital mammography is widely recognized as an efficient and accurate tool for the early and accurate detection of breast abnormalities. The healthcare imaging sector continues to actively design and develop a reliable, error-free computer-aided diagnostic (CAD) system for automated diagnosis of digital mammograms for the past few decades. Traditional approaches to the clinical examination of mammogram images often require significant labor costs and are highly dependent on clinical expertise, leading to inherent variability. In contrast, Artificial Intelligence (AI), including Machine Learning (ML) and Deep Learning (DL) techniques, has emerged as a valuable technology in breast cancer diagnosis using mammography images to reduce diagnostic error rates and improve accuracy. Taking this fact into account, this paper reviews the current state of research on digital mammogram detection and classification using ML and DL techniques. It emphasizes explicitly recent advancements in ML and DL techniques for analyzing digital mammography images and offers perspectives on future developments.