Chest X-ray multi-label disease classification pipeline using a CNN ensemble of DenseNet-121, VGG19, and EfficientNetV2-S models with Grad-CAM explainability. Preprocessed 320x320 input images flow through parallel fine-tuned deep learning models that output probabilities for 14 pathology classes, thresholded at 0.5 for binary predictions. Grad-CAM heatmaps overlay on original images to visualize which regions drive each disease prediction, enabling clinicians to understand model confidence. Fork this diagram on Diagrams.so to customize model architectures, adjust thresholds, or integrate with AWS SageMaker for production deployment. This architecture demonstrates best practices for interpretable AI in healthcare—critical for regulatory compliance and clinical adoption.