Harnessing Deep Learning for Early Breast Cancer Diagnosis: A Review of Datasets, Methods, Challenges, and Future Directions

Abstract

Breast cancer is the most common kind of cancer diagnosed worldwide and the leading cause of cancer-related deaths among women, therefore it presents a significant public health risk. Therefore, early identification and diagnosis of malignant breast tumors can significantly increase patient survival rates and facilitate effective treatment. Imaging is one of the key procedures in decision-making for diagnosing breast cancer. In instance, mammography is the most efficient and highly recommended imaging technique by radiologists in the identification of many types of breast abnormalities However, with the daily growth in mammography, it is still challenging for radiologists and doctors to give correct and consistent interpretations, which can lead to potential misinterpretations and unneeded biopsies. Statistics show that substantial portions, ranging from 10% to 30%, of incorrect diagnoses in medical image analysis are the result of human error. Considering this context, various researchers have looked into the use of mammography and Deep Learning (DL) approaches for accurate early breast cancer diagnosis. Utilizing these approaches in clinical settings can increase diagnosis accuracy, save time spent, lower the likelihood of mistakes and errors, increase patient satisfaction, and streamline radiologists' workloads. The basic ideas of healthy breast tissue, breast cancer, mammography, and deep learning are briefly presented in this review. This paper delves into the latest advances in systems utilizing deep learning algorithms applied to breast cancer diagnosis using mammograms. Additionally, it provides a concise overview of publicly available mammogram datasets and explores the most widely used metrics for evaluating computer-aided breast cancer diagnosis systems.. Finally, issues and potential research objectives in this developing field are outlined. This paper presents a comprehensive examination of the topic and intend to inspire and direct medical professionals, researchers, scientists, and other healthcare workers who are interested in creating cutting-edge applications toward early breast cancer diagnosis using mammographies image processing in the right direction.