Brain Tumor Detection with AI

This project combines cutting-edge techniques in image preprocessing, feature extraction, and robust classification models to achieve 93% accuracy in detecting brain tumors.

Key Features:

1️⃣ Image Preprocessing: Techniques like CLAHE, Otsu thresholding, and skull stripping enhance image quality and isolate the brain region.

2️⃣ Feature Engineering: GLCM and HOG extract critical texture and shape information.

3️⃣ Modeling: A powerful ensemble model (SVM + KNN) ensures robust classification performance.

Results:

SVM: 93% accuracy

KNN: 90% accuracy

Ensemble Model: 93% accuracy with improved precision and recall metrics.