Brain Tumor Classification
Medical imaging classification using CNN and zoning technique achieving 97% accuracy.
Overview
A medical imaging project focused on classifying brain tumors from MRI scans using convolutional neural networks combined with a novel zoning preprocessing technique. The model achieves 97% classification accuracy by dividing input images into zones and extracting spatial features before feeding them through the CNN pipeline.
The project demonstrates domain-specific ML expertise, careful model validation with sensitivity/specificity analysis, and awareness of medical data handling requirements.
Key Features
Technical Highlights
Achieved 97% classification accuracy on medical imaging
Implemented novel zoning technique for spatial feature extraction
Validated model against medical diagnostic standards