🧠 Brain Tumor Detection Using Deep Learning

This project is a deep learning-based image classification system that detects the presence and type of brain tumor from MRI scans. It uses a custom CNN architecture as well as EfficientNetB0, a state-of-the-art pre-trained model, to classify brain MRI images into multiple categories.

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🔍 Project Overview

Brain tumors can be life-threatening if not diagnosed early. Manual diagnosis via MRI images is time-consuming and can vary based on expertise. This project leverages the power of deep learning to automate the classification of brain tumors with high accuracy.

📂 Dataset

• The dataset used consists of labeled brain MRI images.
• Images are categorized into:
  • Glioma
  • Meningioma
  • Pituitary tumor
  • No tumor

(Note: The dataset is not included in this repository due to size and copyright. You can use any publicly available brain tumor MRI dataset.)

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🧠 Techniques Used

• Image Preprocessing: Resizing, normalization, augmentation
• Custom CNN Model: Built using Keras & TensorFlow
• EfficientNetB0: Fine-tuned with transfer learning
• Model Evaluation: Accuracy, Confusion Matrix, Classification Report

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📦 Project Structure

brain-tumor-detection/ │ ├── EfficientNetB0_model.ipynb # Notebook using pre-trained model ├── Custom_CNN_model.ipynb # Notebook using custom CNN ├── requirements.txt # List of required libraries ├── README.md # Project description │ ├── models/ # (Optional) Saved model weights ├── images/ # Sample input images └── dataset/ # Directory for your MRI image dataset

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🛠️ How to Run the Project

Step 1: Clone the Repository

git clone https://github.com/your-username/brain-tumor-detection.git
cd brain-tumor-detection

Step 2: Install Required Libraries

pip install -r requirements.txt

Or manually install:

pip install tensorflow keras numpy pandas matplotlib seaborn opencv-python scikit-learn

Step 3: Run the Notebooks

Open either EfficientNetB0_model.ipynb or Custom_CNN_model.ipynb in Jupyter Notebook or Google Colab

Modify the dataset path as needed in the code

Run the cells to train and evaluate the models

✅ Output

After training, the model produces:

Accuracy and loss graphs

Confusion matrix for visualizing predictions

A classification report with precision, recall, and F1-score

🚀 Future Enhancements

Add a user-friendly web interface for uploading images

Deploy the model for live prediction

Test with a larger dataset and try Vision Transformer-based models

🧰 Tools & Technologies

Python

TensorFlow / Keras

OpenCV

Scikit-learn

Jupyter Notebook / Google Colab

EfficientNet (via Keras Applications)

✨ Acknowledgments

Dataset from Kaggle: Brain MRI Images for Brain Tumor Detection

Inspired by various open-source ML projects for health diagnostics.

⚠️ Disclaimer

This project is intended for educational purposes only. It is not suitable for medical diagnosis or treatment decisions.

📸 Sample Output

Tumor Image:

Result Image:

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📫 Contact Me

📧 Email: jaindhaani0919@gmail.com 💼 LinkedIn: www.linkedin.com/in/dhaani-jain-09b9482a0
💻 GitHub: https://github.com/deejay-eng 🌐 Portfolio: https://deejay-eng.github.io/Portfolio/

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