Hi Rabeah,
Here are several methods you can use to interpret your CNN model:
1. Visualization Techniques
a. Saliency Maps - Saliency maps highlight the parts of the input that are most important for the CNN's decision. In MATLAB, you can use the `deepDreamImage` function to visualize the activations of different layers.
b. Grad-CAM (Gradient-weighted Class Activation Mapping) - Grad-CAM provides a coarse localization map highlighting the important regions in the input. MATLAB does not have a direct function for Grad-CAM, but you can implement it using the gradient of the output with respect to the feature maps.
2. Feature Importance
a. Permutation Feature Importance - This method involves shuffling the values of each feature and measuring the change in model performance.
3. Layer-wise Relevance Propagation (LRP) - LRP decomposes the prediction into contributions of each input feature. This method is more complex to implement but provides detailed insights into the decision-making process.
4. Explainable AI (XAI) Libraries
a. LIME (Local Interpretable Model-agnostic Explanations) - LIME approximates the model locally with an interpretable model. You can use Python libraries like `lime` to implement this.
b. SHAP (SHapley Additive exPlanations) - SHAP values explain the output of a model by computing the contribution of each feature. This can be done using Python libraries like `shap`.
Hope this helps.
