A novel CNN architecture for breast cancer detection

Abstract

Breast cancer is a leading cause of mortality among women worldwide, and early detection is critical for improving survival rates. While mammography is a key screening tool, its accuracy can be impacted by human interpretation. Convolutional Neural Networks (CNNs) offer advanced image analysis capabilities to enhance early detection and support healthcare professionals with higher accuracy and reliability. This study presents a novel CNN architecture, developed from scratch, to automate breast cancer detection and improve diagnostic accuracy. Using the MIAS and INBREAST datasets with advanced data augmentation techniques, the model demonstrates outstanding performance. On the MIAS dataset, it achieves an accuracy of 0.9912, recall of 0.9912, precision of 0.9914, AUC of 0.9996, and F1-score of 0.9912. Similarly, on the INBREAST dataset, the model achieves an accuracy of 0.9494, recall of 0.9494, precision of 0.9529, AUC of 0.9937, and F1-score of 0.9493, highlighting the accuracy and reliability across different datasets. The findings illustrate the potential of deep learning-based computer-aided diagnostic (CAD) systems in improving early breast cancer detection, reducing errors, and enhancing the cost-efficiency of providing healthcare.