About This Architecture

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.

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How do you build an interpretable multi-label chest X-ray classification system using deep learning ensemble models?

This diagram shows a CNN ensemble combining DenseNet-121, VGG19, and EfficientNetV2-S to classify 14 pathology classes from preprocessed 320x320 chest X-rays. Grad-CAM heatmaps overlay on original images to visualize which regions drive predictions, enabling clinicians to validate model reasoning. Thresholding at 0.5 converts probabilities to binary labels while maintaining confidence scores for c