SpineVision AI - Spine Disease Detection from X-Ray
An AI-powered diagnostic web platform that detects spinal conditions from uploaded X-ray images using a DenseNet-121 + YOLOv9 dual-model pipeline - giving healthcare professionals instant, AI-assisted analysis with a results history and admin oversight dashboard.
Key Outcome
Instant spine X-ray analysis
Dual-model AI pipeline delivers classified spinal findings in seconds - deployed and live.
Radiological interpretation of spinal X-rays is time-intensive and requires specialist expertise. Hospitals and clinics in lower-resource settings lack fast, accessible tools to flag abnormal findings before specialist review.
Built SpineVision AI as a full-stack web application where clinicians upload X-ray images (PNG, JPG, or DICOM) and receive instant AI analysis from a dual-model pipeline combining DenseNet-121 for classification and YOLOv9 for localisation. Results are stored per user, and an admin panel provides real-time oversight of total users, scan volume, today's scans, normal vs abnormal counts, and system status.
Key Features
DenseNet-121 + YOLOv9 dual-model pipeline for X-ray classification and localisation
Drag-and-drop X-ray upload supporting PNG, JPG, and DICOM formats up to 50 MB
Real-time analysis with animated progress indicator and result delivery in seconds
Per-user scan history with result archive and retrieval
Admin panel: total users, total scans, today's scans, normal/abnormal result counts
Real-time system status monitoring: pending scans, completed today, abnormal flags
End-to-end encrypted uploads with role-based access control
Deployed on Vercel for global accessibility from any browser
Technology Stack
Project Visuals
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