PneuNet: A Multi-Scale Attention-Enhanced CNN for Pediatric Pneumonia Detection from Chest X-rays
Irfan Sadiq Rahat, et al.
IEEE DELCON 2025 (4th Delhi Section Conference) · Paper #235 · Accepted
PneuNet is a lightweight multi-scale attention CNN for binary classification of pediatric chest X-rays into Normal and Pneumonia. Designed for resource-constrained clinical settings with high sensitivity as the primary optimization target (minimizing false negatives critical in pediatric care).
- Multi-scale feature extraction (parallel 3×3 and 5×5 branches)
- Spatial + channel attention for lesion localization
- High sensitivity optimization (recall for Pneumonia class)
- Lightweight: ~4M parameters, suitable for edge deployment
- Grad-CAM visualization for clinical interpretability
| Metric | Value |
|---|---|
| Accuracy | 95.7% |
| Sensitivity (Pneumonia) | 98.1% |
| Specificity | 92.4% |
| AUC | 0.973 |
Kaggle Chest X-Ray Pneumonia Dataset (5,856 images) Download: kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia
Classes: Normal (1,583) · Pneumonia (4,273)
git clone https://github.com/IrfanSadiqRahat/PneuNet-PediatricPneumonia.git
cd PneuNet-PediatricPneumonia
pip install -r requirements.txt
python train.py --data_dir data/chest_xray
python evaluate.py --checkpoint outputs/best_model.pth --gradcam@inproceedings{rahat2025pneunet,
title={PneuNet: A Multi-Scale Attention-Enhanced CNN for Pediatric Pneumonia Detection from Chest X-rays},
author={Rahat, Irfan Sadiq and others},
booktitle={Fourth IEEE Delhi Section Conference (DELCON 2025)},
year={2025},
organization={IEEE}
}