How chando0185/chest_x_ray_detection works
This is a desktop chest-X-ray pneumonia classifier. The user selects an image, the app loads the saved model, preprocesses the image, predicts a class, and announces the result through speech. A separate notebook-derived script trains and saves the model. The README gives only the project title, so these capabilities and connections are inferred from the supplied source; the generated model file and dataset are not in the tree.
Main entry points: chest_xray.py Image selection · Untitled.py VGG16 training
Components 11
Desktop app
- Qt application
chest_xray.py - Image selection
chest_xray.py - Result announcement
chest_xray.py - File dialog
chest_xray.py
Image inference
- Image preprocessing and prediction
chest_xray.py - Saved model
Model training
- VGG16 training
Untitled.py
Other components
- User
- Selected image
- Windows speech
- Training and validation images
Connections 12
- User → Qt application: uses
- Qt application → Image selection: connects upload
- Qt application → Result announcement: connects prediction
- Image selection → File dialog: opens
- File dialog → Selected image: selects
- Image selection → Saved model: loads
- Image selection → Selected image: loads
- Image selection → Image preprocessing and prediction: preprocesses and predicts
- Image preprocessing and prediction → Saved model: uses
- Result announcement → Windows speech: announces result
- Training and validation images → VGG16 training: provides images
- VGG16 training → Saved model: saves