# Training Details

## Training Data
- Open-access chest X-ray datasets (e.g., NIH ChestX-ray14, CheXpert).  
- Data preprocessing: normalization, resizing, augmentation.  

## Training Procedure
- **Stage 1**: EfficientNet-B0 for coarse classification (normal vs abnormal).  
- **Stage 2**: EfficientNet-B2 for expert-level multi-label disease classification.  
- **Grad-CAM** integrated for visual interpretability.  

## Training Hyperparameters
- Mixed precision (fp16)  
- Optimizer: AdamW  
- Learning rate scheduler: CosineAnnealing  
- Loss: Weighted BCE with logits  

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# Evaluation

## Testing Data
- Evaluated on public benchmark datasets (CheXpert, NIH ChestX-ray14).  

## Metrics
- AUROC (per-class and mean)  
- F1-score  
- Sensitivity/Specificity  

## Results
- Mean AUROC ≈ **0.85–0.90** (depending on dataset and task)  
- Grad-CAM heatmaps align with radiologically relevant regions  

## Model Examination
- Grad-CAM visualizations available for each prediction  
- Two-stage pipeline mirrors clinical workflow  

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# Environmental Impact
- **Hardware Type**: NVIDIA Tesla V100 (cloud GPU)  
- **Hours used**: ~60 GPU hours  
- **Cloud Provider**: Google Cloud  
- **Compute Region**: US-central  
- **Carbon Emitted**: Estimated ~25 kg CO2eq  

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# Technical Specifications

## Model Architecture
- Stage 1: EfficientNet-B0  
- Stage 2: EfficientNet-B2  
- Hierarchical classification pipeline  
- Grad-CAM interpretability module  

## Compute Infrastructure
- Hardware: NVIDIA V100 (16GB)  
- Software: PyTorch, Hugging Face Transformers, CUDA 11.8  

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# Citation

## BibTeX
```bibtex
@article{indabax2025cxrnet,
  title={Hierarchical CXR-Net: A Two-Stage Framework for Efficient and Interpretable Chest X-Ray Diagnosis},
  author={Ssempeebwa, Phillip and IndabaX Uganda AI Research Lab},
  year={2025},
  journal={Digital Health Africa 2025 Poster Proceedings}
}