Advanced Deep Learning Strategies for Breast Cancer Image Analysis

Breast cancer (BC) is a leading cause of cancer-related deaths in women, but early detection significantly improves survival rates. Recently, deep learning neural networks have shown potential for enhancing BC screening, identification, and classification. In this research, we propose a modified deep learning model incorporating customized architecture adjustments and advanced data augmentation techniques for early BC detection and classification from mammography images. Our model achieves a 40% increase in accuracy compared to state-of-the-art transfer learning methods on the MIAS and SA datasets, demonstrating improved robustness and precision. However, while our results are promising, the limited size and diversity of the datasets suggest that further studies are needed to validate the generalizability of the model across broader, more varied data sources.