Brain tumor detector

This project uses deep learning to detect brain tumors from MRI images. The dataset contains two classes: "Yes" (tumor present) and "No" (no tumor). The U-Net model is trained to segment and classify tumors, aiding in early diagnosis.

Key Steps

Data Preparation: Loaded, normalized, and split the dataset.

Image Preprocessing: Applied augmentation and created tumor masks.

Model Development: Used U-Net CNN for segmentation and classification.

Evaluation: Measured IoU, Dice Coefficient, Accuracy, Precision, and Recall.