Overview
Built a deep learning-based medical imaging pipeline for detecting PUJ obstruction using ultrasound data. The project utilized transfer learning with state-of-the-art architectures and implemented advanced explainability tools.
Key Features
- Deep learning-based detection
- Transfer learning implementation
- Model explainability
- Medical imaging processing
- Performance optimization
Technical Implementation
Model Architecture
- Implemented VGG16
- Integrated InceptionV3
- Utilized DenseNet121
- Transfer learning optimization
Explainability Tools
- t-SNE visualization
- Activation maps
- Model interpretation
- Performance analysis
Data Processing
- Ultrasound data preprocessing
- Image augmentation
- Batch processing
- Validation pipeline
Tech Stack
- Python
- TensorFlow
- Keras
- OpenCV
- VGG16
- InceptionV3
- DenseNet121
- t-SNE
Impact
- Enhanced detection accuracy
- Improved diagnosis support
- Model interpretability
- Efficient processing pipeline
Project Gallery
[Project screenshots to be added]