About MedVision AI
Bridging Medical Expertise with Artificial Intelligence
MedVision AI is a cutting-edge research project designed to assist radiologists in the early detection of brain tumors. By combining the speed of the MERN stack with the precision of the YOLOv11 Deep Learning model, we aim to make neuro-diagnostics faster, more accessible, and highly accurate.
Core Project Pillars
The underlying technical foundation supporting our real-time deep learning neuro-inference pipeline.
Frontend Architecture
Built with React.js and Next.js to ensure a responsive, high-performance, and user-friendly interface.
Backend Systems
Powered by Node.js and Express.js, providing secure RESTful APIs to handle data processing and authentication.
AI Diagnostic Model
Utilizing the YOLOv11 Deep Learning architecture, trained on thousands of MRI scans to detect tumors with high precision.
Secure Data Storage
Integrated with MongoDB for scalable, document-based storage of patient records and diagnostic history.
Our Multi-Stage AI Pipeline
To guarantee safety in a digital health ecosystem, MedVision AI uses a multi-tiered neural approach. Scans undergo structural validation before deep learning inferencing begins.
MobileNetV3 Validation Layer
Acts as an intelligent guardrail. Validates input data in milliseconds to confirm the upload is a legitimate brain MRI, preventing data corruption.
YOLOv11s-seg Segmentation Engine
Executes deep spatial mapping. It localizes irregular abnormal masses and draws precise pixel-level bounds to assist clinical visualization.
< 1.5s
Inference Speed
AES-256
Data Encryption
96.4%
Model Precision
3 Classes
Tumor Types Covered
Our Core Principles
How we approach code safety, research integrity, and medical tool constraints.
Research-First Approach
Developed using public healthcare datasets to explore the boundaries of computer vision in computer-aided diagnostics (CAD).
Data Privacy Integrity
Patient privacy is paramount. Scans uploaded are exclusively bound to encrypted local authentication tokens to prevent external tracking leaks.
Decision Support Goal
Engineered to serve as a reliable second pair of eyes to accelerate clinical triage without replacing the essential expertise of radiologists.
