Brain Tumor Detection System
Deep learning system achieving 93% accuracy on 19K+ MRI scans using multi-model ensemble (CNN, VGG16, YOLOv8).
Completed
December 2025
Duration
5 months
Role
ML Engineer & Team Lead
Team
Team of 3
Problem
Manual MRI analysis is slow and subjective. Radiologists need automated assistance for faster, more consistent tumor detection.
Solution
Built a multi-model pipeline comparing CNN, VGG16, VGG19, and YOLOv8 architectures with transfer learning and data augmentation across 19K+ images.
Impact
93% accuracy on test dataset. Real-time classification into Glioma, Meningioma, and Pituitary tumor types with confidence scores.
About This Project
A comprehensive deep learning system for detecting and classifying brain tumors from MRI scans. The project processes over 19,000+ MRI images using state-of-the-art computer vision models.
Implemented multiple architectures including CNN, VGG16, VGG19, and YOLOv8 for comparison and ensemble predictions. Achieved 93% accuracy on the test dataset.
The system provides real-time tumor detection, classification into tumor types, and confidence scores for medical professionals.
Key Features
Technical capabilities and highlights
Multi-model ensemble for improved accuracy
Real-time MRI scan analysis
Tumor type classification (Glioma, Meningioma, Pituitary)
Confidence score visualization
Data augmentation for robust training
Transfer learning with pre-trained models
93% accuracy on test dataset
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