CBAM-Enhanced DenseNet121 for Multi-Class Chest X-Ray Classification with Grad-CAM Explainability

Pneumonia remains a leading cause of childhood mortality worldwide, with a heavy burden in low-resource settings such as Bangladesh, where radiologist availability is limited. Most existing deep learning approaches treat pneumonia detection as a binary problem, overlooking the clinically critical distinction between bacterial and viral aetiology. This paper proposes CBAM-DenseNet121, a transfer-learning framework that integrates the Convolutional Block Attention Module (CBAM) into DenseNet121 for three-class chest X-ray classification: Normal, Bacterial Pneumonia, and Viral Pneumonia. We also conduct a systematic binary-task baseline study revealing that EfficientNetB3 (73.88%) underperforms even the custom CNN baseline (78.53%), a practically important negative finding for medical imaging model selection. CBAM-DenseNet121 achieves 84.46% test accuracy with per-class AUC scores of 0.9504, 0.9658, and 0.9220 for bacterial pneumonia, normal, and viral pneumonia respectively. Grad-CAM visualisations confirm that the model attends to anatomically plausible pulmonary regions for each class, supporting interpretable deployment in resource-constrained clinical environments. Index Terms — Chest X-ray; pneumonia classification; CBAM attention; DenseNet121; transfer learning; GradCAM; convolutional neural network; medical image classification.