Abstract
This study proposes a weakly supervised deep learning framework for pneumonia classification and localization from chest X-rays, utilizing Grad-CAM explanations. Instead of costly pixel-level annotations, our approach utilizes image-level labels to generate clinically meaningful heatmaps that highlight regions affected by pneumonia. We evaluate seven ImageNet-pretrained architectures ResNet-18/50, DenseNet-121, EfficientNet-B0, MobileNet-V2/V3, and ViT-B16 under identical training conditions with focal loss and patient-wise splits to prevent data leakage. Experimental results on the Kermany CXR dataset demonstrate that ResNet-18 and EfficientNet-B0 achieve the best overall test accuracy of 98\%, ROC-AUC = 0.997, and F1 = 0.987, while MobileNet-V2 provides an optimal trade-off between accuracy and computational cost. Grad-CAM visualizations confirm that the proposed models focus on clinically relevant lung regions, supporting the use of interpretable AI for radiological diagnostics. This work highlights the potential of weakly supervised explainable models that enhance pneumonia screening transparency, and clinical trust in AI-assisted medical imaging. this https URL
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URL
https://arxiv.org/abs/2511.00456
