DeepLens-SE-ResNet Optimizing Attention Mechanisms for Breast Cancer Detection

Predicting breast cancer has become a top medical research goal due to the widespread use of deep learning techniques in the interpretation of medical pictures. The anticipated accuracy of Res Net (Residual Net- works) in distinguishing between malignant and non-cancerous is improved by this study. In particular, propose RESNET-SENET, a hybrid architecture that integrates the SE block into the residual blocks of ResNet, to com- bine the benefits of ResNet and SE-Net. The integration enhances the model's ability to extract critical information from regions associated with breast cancer by allowing it to absorb more information from its environ- ment and focus on specific aspects. This hybrid architecture makes use of SE-Net's channel recalibration capa- bilities while maintaining ResNet's computational efficiency and architectural simplicity. To increase the mod- el's performance, expand the training dataset, enhance the model's generalization capabilities, and provide more reliable support for the accurate diagnosis of breast cancer, an effective data augmentation approach is used. The proposed SE-Net-based model performs better in this prediction task, with an accuracy of 94%, which is higher than the ResNet model's 91% accuracy.