Early Detection of Breast Cancer with Microcalcifications on Mammography Using Deep Learning

Breast cancer is among the leading causes of death in women globally, making early detection critical for improving survival rates. Microcalcifications (MCs) on mammographic images are important markers for early detection of breast cancer; yet, identifying and interpreting them can be difficult. Conventional diagnostic procedures sometimes face obstacles due to the complexity and nuance of MC patterns, resulting in higher percentages of missed diagnoses and false positives. This paper develops a deep convolutional neural network (CNN) model to increase the detection and classification accuracy of microcalcifications (MCs) in mammographic images. Dataset of 1,093 mammography images used, the proposed model reaches a remarkable training accuracy of 99.98% and testing accuracy of 90.37%. The model's excellent accuracy and low overfitting highlight its potential to assist radiologists in the early detect and diagnose of breast cancer, thereby improving patient outcomes. This study's contribution is the innovative use of advanced deep learning algorithms to a major issue in medical imaging, which represents a significant improvement over current diagnostic approaches.