I'm exploring optimal CNN architectures for breast cancer detection using deep learning. I want insights into effective feature selection techniques and hyperparameter tuning strategies to enhance model performance and generalization. What are the best approaches for fine-tuning pre-trained models for medical imaging?
Zein Al-Abideen Douba
Feature Selection & Preprocessing: Normalize and augment data to reduce overfitting. Use Grad-CAM or SHAP for feature importance visualization. Apply PCA or UMAP if combining image features with clinical data. Fine-Tuning Techniques: Freeze initial layers of the pre-trained model and gradually unfreeze during training. Use discriminative learning rates for different layers. Implement early stopping, data augmentation, and class balancing (e.g., focal loss) for robust performance. Hyperparameter Optimization: Use Bayesian optimization, GridSearch, or Optuna for tuning learning rate, dropout, batch size, etc
For breast cancer classification using deep learning, pre-trained CNN architectures such as ResNet50, DenseNet121, and EfficientNet-B0/B1 have shown strong performance, especially when fine-tuned on high-quality medical image datasets (e.g., histopathology, ultrasound, or mammography).
Key strategies to enhance performance:
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