Medical AI with Deep Learning
Hybrid CNN achieving 91.2% accuracy on chest X-ray classification with interpretability
The Challenge
Classifying 14 different chest conditions from X-rays while maintaining interpretability for healthcare compliance - a critical requirement that pure deep learning solutions often fail to address.
Innovation
Built a hybrid architecture combining classical computer vision techniques with deep learning:
- Engineered Features: GLCM texture analysis, HOG shape descriptors, frequency domain analysis
- Deep Learning: Transfer learning with ResNet50 backbone
- Attention Fusion: Multi-head attention mechanism to dynamically weight feature sources
Results
- 91.2% accuracy across 14 disease classifications
- Grad-CAM visualizations showing model focus areas for interpretability
- 33% reduction in diagnosis time
- Data efficient: 85% accuracy with just 1,000 training images
Technical Highlights
class HybridMedicalClassifier(nn.Module):
def __init__(self):
self.cnn_branch = MedicalCNN()
self.feature_branch = EngineereedFeatures()
self.attention = nn.MultiheadAttention(256, num_heads=8)
The system learns when to trust engineered features (clear anatomical abnormalities) versus deep features (subtle texture patterns).
Key Learning
In regulated industries like healthcare, interpretability can be more valuable than marginal accuracy gains. The hybrid approach achieved both high performance and explainability.