Explainable Deep Learning in Medical Imaging: Brain Tumor and Pneumonia Detection

Explainability matters for clinical trust: The paper demonstrates that high accuracy alone isn't sufficient for medical AI adoption—doctors need to understand why a model makes its diagnosis. By using Grad-CAM visualization to show which parts of images influenced the AI's decision, the framework addresses a major barrier to getting clinicians to trust and use these tools in practice.

Different accurate models can reason differently: Both ResNet50 and DenseNet121 achieved strong performance (92.5% and 94.3% accuracy for brain tumors), but Grad-CAM revealed they focused on different image regions. DenseNet121 concentrated on actual pathological areas, while ResNet50 sometimes scattered attention to peripheral regions—showing that two "accurate" models may not be equally reliable for clinical use.

Explainability can identify model weaknesses: The visualization approach revealed that ResNet50 sometimes focused on non-pathological areas despite correct predictions, suggesting the model might fail in edge cases or different patient populations. This highlights how explainability tools can catch problems that accuracy metrics alone would miss.

Explainable AI is feasible for medical imaging: The paper shows that adding interpretability features (Grad-CAM heatmaps) to existing deep learning models is practical and doesn't require starting from scratch, making it a realistic path for improving current medical AI systems.