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

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

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

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

Citation

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}
}