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

TL;DR

This paper develops an explainable deep learning framework using Grad-CAM for detecting brain tumors and pneumonia in medical images, enhancing interpretability and clinical trust.

Contribution

It introduces an explainable AI approach combining ResNet50 and DenseNet121 with Grad-CAM for medical image diagnosis, emphasizing interpretability and model comparison.

Findings

DenseNet121 outperformed ResNet50 in accuracy.

Grad-CAM visualizations highlighted core pathological regions.

Models showed high classification performance on Kaggle datasets.

Abstract

Deep Learning (DL) holds enormous potential for improving medical imaging diagnostics, yet the lack of interpretability in most models hampers clinical trust and adoption. This paper presents an explainable deep learning framework for detecting brain tumors in MRI scans and pneumonia in chest X-ray images using two leading Convolutional Neural Networks, ResNet50 and DenseNet121. These models were trained on publicly available Kaggle datasets comprising 7,023 brain MRI images and 5,863 chest X-ray images, achieving high classification performance. DenseNet121 consistently outperformed ResNet50 with 94.3 percent vs. 92.5 percent accuracy for brain tumors and 89.1 percent vs. 84.4 percent accuracy for pneumonia. For better explainability, Gradient-weighted Class Activation Mapping (Grad-CAM) was integrated to create heatmap visualizations superimposed on the test images, indicating the most influential image regions in the decision-making process. Interestingly, while both models produced accurate results, Grad-CAM showed that DenseNet121 consistently focused on core pathological regions, whereas ResNet50 sometimes scattered attention to peripheral or non-pathological areas. Combining deep learning and explainable AI offers a promising path toward reliable, interpretable, and clinically useful diagnostic tools.