Breast cancer (BC) remains the leading cause of cancer-related death all over the world. Early accurate detection is key to the improvement of patient prognosis. The ability of advanced Artificial Intelligence (AI) methods, with a focus on Convolutional Neural Networks (CNNs), to classify breast lesions obtained from mammography and ultrasonography images is addressed in this study. Five of the latest models (ResNet-50, VGG-16, Inception-v3, custom-made CNN, and hybrid model) are evaluated using an integrated and thoroughly labeled dataset containing 10,000 images, focusing on key performance indices (KPIs), including accuracy, sensitivity, and F1-score. Furthermore, the exploration examines the challenges and protocols for integrating Explainable AI (XAI) and higher-performing models into existing clinical screening protocols and addresses issues related to trust, model generality, and ethical deployment. The findings indicate that the maximum classification accuracy (96.2%) and sensitivity of 95.8% were attained by the hybrid CNN architecture, which suggests a robust framework for safe, effective, and clinically integrated AI diagnostic support.
Bannor et al. (Thu,) studied this question.