MRI Brain Tumor Classification

This project implements a machine learning pipeline designed to automate the detection of brain tumors in MRI scans. Utilizing a Random Forest Classifier and computer vision techniques, the model provides an efficient method for binary image classification. Technical Specifications Language: Python Computer Vision: OpenCV (cv2) Machine Learning: Scikit-Learn Numerical Processing: NumPy Core Methodology Image Preprocessing: MRI scans are normalized through grayscale conversion and resized to a consistent 64x64 pixel resolution to ensure feature uniformity. Feature Engineering: Images are flattened into high-dimensional arrays to serve as input vectors for the classification model. Classification Model: A Random Forest architecture is utilized to distinguish between tumor-present and tumor-absent scans. Inference Engine: A dedicated prediction module allows for rapid classification of individual unseen images.