Usefulness of machine learning and deep learning approaches in screening and early detection of breast cancer

Breast cancer (BC) is one of the most common types of cancer in women, and its prevalence is on the rise. The diagnosis of this disease in the first steps can be highly challenging. Hence, early and rapid diagnosis of this disease in its early stages increases the likelihood of a patient's recovery and survival. This study presents a systematic and detailed analysis of the various ML approaches and mechanisms employed during the BC diagnosis process. Further, this study provides a comprehensive and accurate overview of techniques, approaches, challenges, solutions, and important concepts related to this process in order to provide healthcare professionals and technologists with a deeper understanding of new screening and diagnostic tools and approaches, as well as identify new challenges and popular approaches in this field. Therefore, this study has attempted to provide a comprehensive taxonomy of applying ML techniques to BC diagnosis, focusing on the data obtained from the clinical methods diagnosis. The taxonomy presented in this study has two major components. Clinical diagnostic methods such as MRI, mammography, and hybrid methods are presented in the first part of the taxonomy. The second part involves implementing machine learning approaches such as neural networks (NN), deep learning (DL), and hybrid on the dataset in the first part. Then, the taxonomy will be analyzed based on implementing ML approaches in clinical diagnosis methods. The findings of the study demonstrated that the approaches based on NN and DL are the most accurate and widely used models for BC diagnosis compared to other diagnostic techniques, and accuracy (ACC), sensitivity (SEN), and specificity (SPE) are the most commonly used performance evaluation criteria. Additionally, factors such as the advantages and disadvantages of using machine learning techniques, as well as the objectives of each research, separately for ML technology and BC detection, as well as evaluation criteria, are discussed in this study. Lastly, this study provides an overview of open and unresolved issues related to using ML for BC diagnosis, along with a proposal to resolve each issue to assist researchers and healthcare professionals.