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.
