To enhance the interpretability of the proposed Convolutional Neural Network (CNN)-based approach for breast cancer detection, several strategies can be employed. First, integrating techniques such as Grad-CAM (Gradient-weighted Class Activation Mapping) can provide visual explanations of the regions in mammograms that contribute most to the model's predictions. This method highlights the areas of the image that the CNN focuses on when making a classification, allowing radiologists to understand the rationale behind the model's decisions.
Second, utilizing attention mechanisms within the CNN architecture can help the model learn to focus on the most relevant features of the mammogram images, thereby improving both interpretability and performance. By visualizing the attention maps, clinicians can gain insights into which features are deemed significant by the model.
Third, implementing model-agnostic interpretability methods, such as LIME (Local Interpretable Model-agnostic Explanations), can provide local explanations for individual predictions. This approach allows for a better understanding of how specific features influence the model's output, making it easier for medical professionals to trust and validate the model's predictions.
Lastly, incorporating explainable AI (XAI) frameworks can facilitate the development of user-friendly interfaces that present model predictions alongside interpretative insights, fostering collaboration between AI systems and healthcare providers. By enhancing interpretability, the CNN-based approach can not only improve diagnostic accuracy but also increase clinician confidence in automated breast cancer detection systems.