📌 Overview

This project presents a high-performance deep learning pipeline for automated brain tumor classification using MRI images. It employs EfficientNetB3, a cutting-edge convolutional neural network (CNN) architecture, fine-tuned to accurately distinguish between various types of brain tumors.

Our goal is to provide a fast, reliable, and scalable solution that can assist medical professionals in making informed diagnostic decisions, reducing manual workload, and improving early detection rates.

🚀Highlights

🎯 High-accuracy multi-class classification of brain tumors.

⚡ Powered by EfficientNetB3, known for its efficiency and superior performance.

🧹 Built-in data preprocessing and augmentation to enhance generalization.

📊 Rich metrics visualization and confusion matrix for evaluation insights.

🔁 Modular design for seamless training, evaluation, and deployment.

🌐 Includes an interactive Streamlit web app.

🧠 Tumor Classes The model classifies MRI brain scans into the following four categories:

Glioma Tumor

Meningioma Tumor

Pituitary Tumor

No Tumor

📈 Model Performance

Metric Score

Accuracy ✅ 97%+

Precision ✅ High

Recall ✅ High

F1-Score ✅ Balanced

🙌 Contributing

We welcome all kinds of contributions! Whether it's bug fixes, suggestions, or adding new features — feel free to fork the repo and submit a pull request.