🧠 Brain Tumor Detection using Deep Learning and Machine Learning

Detection of Brain Tumor from MRI images through Deep Learning and classification of tumor types.

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📘 Overview

This project implements a complete pipeline for brain tumor detection from MRI images, inspired by the research paper:
"MRI Brain Tumor Detection using Deep Learning and Machine Learning Approaches" (Anantharajan et al., 2024).

The system classifies MRI images into four categories:

• 🧩 Glioma
• 🧠 Meningioma
• 🧍 Pituitary Tumor
• ✅ No Tumor

The pipeline includes preprocessing, segmentation, feature extraction, and hybrid classification using CNN and SVM.

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✨ Features

• 🔹 CLAHE – Improves MRI image contrast adaptively.
• 🔹 Fuzzy C-Means (FCM) – Segments the tumor region effectively.
• 🔹 GLCM Features – Extracts texture-based statistical features.
• 🔹 CNN-based Deep Features – Learns high-level image features.
• 🔹 EDN-SVM Classifier – Combines deep + texture features using a Support Vector Machine.

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🧰 Tech Stack

Category	Tools/Libraries
Deep Learning	TensorFlow, Keras
Machine Learning	Scikit-learn
Image Processing	OpenCV, Scikit-image, fcmeans
Visualization	Matplotlib, Seaborn
Utilities	NumPy, tqdm

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⚙️ Installation & Setup

1. Clone the repository

   git clone https://github.com/<your-username>/BrainTumorDetection.git
   cd BrainTumorDetection
   

README — How to run locally

1. Install backend dependencies (recommended inside a venv):

cd backend
python -m venv venv
source venv/bin/activate # on windows: venv\Scripts\activate
pip install flask pillow numpy
python app.py

2. Install frontend and run

cd frontend # where package.json is
npm install
npm start