Multi-Label Chest X-Ray Classification with CNNs, Transformers, and Grad-CAM: An Explainable AI Approach

This study evaluates advanced deep learning models — specifically CNNs (EfficientNetB2 and DenseNet201) and transformers (DeiT and ViT) — for the multi-label classification of chest X-ray images, with a primary focus on AI model development and optimization. Using the small CheXpert dataset for training/validation and the original CheXpert test set for evaluation, the research addresses all 14 labels (12 thoracic pathologies, “No Findings,” and “Support Device”), tackling the inherent challenges of overlapping pathologies in multi-label classification. While DeiT showed the highest performance, EfficientNetB2 was chosen for further refinement because it surpassed DenseNet201 on overall metrics and, along with DenseNet201, supports Grad-CAM for post-hoc explainability. Grad-CAM attention maps identified clinically relevant regions in correctly classified cases and pinpointed areas needing improvement in misclassified instances, thereby providing transparency into the models’ decision-making processes. Overall, despite the limitations imposed by lower image resolution and task complexity, the study demonstrates the adaptability of these deep learning models and contributes to the development of trustworthy AI systems in medical imaging that effectively balance diagnostic accuracy with interpretability. Our proposed method achieved promising results with 0.81 accuracy, 0.79 AUC, and integrates explainable AI by providing visual explanations through attention maps using Grad-CAM to enhance model interpretability.