CPU-optimized regularized convolutional neural network architecture for multimedia-based breast cancer metastasis detection

Abstract

The histopathological diagnosis of breast cancer metastases, a subjective and lengthy process, benefits from advances in artificial intelligence for its automation. This study proposes an automatic classification method based on a convolutional neural network (CNN) architecture with progressive regularization (Batch normalization and adaptive dropout), trained on the PatchCamelyon (PCam) dataset. Our approach integrates a data augmentation strategy specific to medical images and advanced optimization for CPU environments, allowing us to balance performance, robustness, and computational accessibility. The architecture comprises only 94,000 parameters, 223 times fewer than leading models. Multi-criteria evaluation shows competitive results: area under the curve (AUC) of 92.92%, F1-score of 82.39%, accuracy of 88.74%, and specificity of 90.61%. A bootstrap robustness analysis provides 95% confidence intervals (AUC: [89.56% 92.91%]), and an ablation study quantifies the reduction in overfitting through progressive dropout (33.6%). This research validates that a well regulated and CPU-optimized CNN architecture can achieve clinically relevant performance while remaining accessible on standard environments, thus opening up concrete prospects for the development of practical AI tools in digital pathology.