Brain Tumor Detection using ResNet-50

This project aims to detect brain tumors from MRI images using a Convolutional Neural Network (CNN) based on the ResNet-50 architecture. The model classifies images into four categories: glioma, meningioma, no tumor, and pituitary.

Model Performance Summary

• Training Accuracy: 99.39%
• Validation Accuracy: 87.82%
• Test Accuracy: 91.99%
• Test Loss: 0.28

Model Details

• Architecture: ResNet-50 v1.5
• Pre-trained on: ImageNet
• Input Image Size: 224x224 pixels
• Normalized Pixel Values: [0, 1]

Callbacks Used

• ModelCheckpoint: Save the best model based on validation loss
• EarlyStopping: Stop training if validation loss doesn't improve for 10 epochs

Training Process

• Epochs: Stopped at 20 epochs due to early stopping
• Batch Size: 16
• Optimizer: Adam with learning rate 0.001
• Loss Function: Sparse Categorical Crossentropy

Getting Started

Prerequisites

• Python 3.x
• TensorFlow
• NumPy
• OpenCV
• Matplotlib
• Kaggle API (for dataset)