Abstract
This paper presents a novel deep learning framework for the early detection of pulmonary abnormalities in chest X-ray images. Our approach combines transfer learning with custom network architectures to achieve state-of-the-art performance on the NIH Chest X-ray dataset. The proposed model demonstrates 94.7% accuracy with a sensitivity of 92.3%, outperforming existing methods while maintaining computational efficiency.
Methodological Innovation
Hybrid architecture combining ResNet-50 feature extraction with attention mechanisms
Clinical Relevance
Validated on 112,000 images from 5 clinical centers across North America
Performance
AUC-ROC of 0.97 for pneumonia detection, surpassing radiologist performance
Methodology
Architecture Overview
Our three-stage pipeline combines preprocessing, feature extraction, and classification modules. The attention gate mechanism focuses on pathological regions while suppressing irrelevant background information.
Training Protocol
- ✓ Adam optimizer with cosine learning rate decay
- ✓ 5-fold cross validation strategy
- ✓ Class-balanced loss weighting
- ✓ Mixed precision training on 4×V100 GPUs
Results & Analysis
| Model | Accuracy | Sensitivity | Specificity |
|---|---|---|---|
| Proposed | 94.7% | 92.3% | 95.1% |
| ResNet-50 | 89.2% | 85.6% | 90.3% |
References
- Wang et al., "Deep learning for medical image analysis", Nature Medicine, 2021
- Johnson et al., "Attention mechanisms in radiology", CVPR, 2022
- NIH Chest X-Ray Dataset, Version 3.0, 2023