How manoj914822/glioma-project works
GLIOMA is a local Flask application for brain-scan tumor analysis, account-based history, and supporting medical guidance. The documented user flow is to upload a scan, analyze it, and review predictions and visualizations; the app also documents analytics, reports, a chatbot, and collaboration. The sampled code confirms Flask routes, SQLite prediction persistence, and chatbot handling. Other feature connections follow the README, since the relevant app sections and templates were not fully sampled.
Main entry points: app.py Scan analysis flow · templates Web interface · uploads Uploaded scans · Imageprocesing Image processing
Components 14
Web experience
- Flask application
app.py - Web interface
templates - Uploaded scans
static/uploads
Scan analysis
- Scan analysis flow
app.py - Image processing
Imageprocesing - Tumor classification
app.py - Grad-CAM views
app.py - Analysis results
app.py
Records and insights
- SQLite records
app.py - Performance analytics
app.py - Downloadable reports
app.py
Medical support
- Medical chatbot
app.py - Scan collaboration
app.py
Other components
- Patient or clinician
Connections 15
- Patient or clinician → Web interface: uses
- Web interface → Flask application: submits requests
- Flask application → Scan analysis flow: dispatches analysis
- Scan analysis flow → Uploaded scans: stores scan
- Scan analysis flow → Image processing: processes image
- Scan analysis flow → Tumor classification: runs classification
- Scan analysis flow → Grad-CAM views: generates visualization
- Scan analysis flow → SQLite records: saves prediction
- Scan analysis flow → Analysis results: returns findings
- Web interface → Analysis results: displays results
- Flask application → SQLite records: reads prediction history
- Flask application → Performance analytics: builds charts
- Flask application → Downloadable reports: creates reports
- Web interface → Medical chatbot: sends questions
- Web interface → Scan collaboration: opens scan collaboration