🧠 NeuroAssist-AI

A Deep Learning-based Smart System for Brain Tumor Detection and Glioma Stage Prediction

🔗 Live Demo

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📌 Overview

NeuroAssist-AI is a two-stage deep learning pipeline that automates:

1. Brain Tumor Detection from grayscale MRI/CT images using a custom CNN.
2. Glioma Stage Prediction using numerical gene mutation data via a custom ANN.

This system is inspired by real clinical practices and aims to provide intelligent support to radiologists and neurologists.

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🧪 Live Testing

You can test the full system online:

🌐 🔗 Live Web App – NeuroAssist-AI

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📚 Research Basis

📄 Inspired by: “Brain Tumor Classification and Glioma Stage Prediction Using Deep Learning”

🔗 Read the original paper

Note: Original paper had no public dataset or code - we implemented it from scratch.

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📂 Dataset

Dataset: Brain Tumor MRI Dataset – Kaggle

• 4 Classes: Glioma, Meningioma, Pituitary, No Tumor
• Format: Grayscale .jpg images categorized in folders

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🧠 Model Architecture

🔷 CNN – Brain Tumor Detection

Layer Type	Description
Input	Grayscale MRI/CT image
Conv Blocks	3 × Conv2D + ReLU + MaxPooling
FC Layers	Flatten → Dense → Softmax
Output	4 classes (No Tumor, Meningioma, Pituitary, Glioma)

✅ Trained from scratch in PyTorch ❌ No dropout (no overfitting observed)

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🟢 ANN – Glioma Stage Classification

Layer Type	Description
Input	Gene mutation test results
Dense	2–3 Fully Connected Layers
Activation	ReLU + Softmax/Regression Output
Output	Glioma Stage (I–IV)

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💾 Model Files

File Name	Purpose	Availability
BTD_model.pth	Brain Tumor Detection (CNN)	🔗 Download from Google Drive
glioma_stages.pth	Glioma Stage Classification (ANN)	✅ Included in models/ directory

📍 Note: Due to GitHub’s 100MB limit, BTD_model.pth is stored externally. After downloading, manually place it inside the models/ folder like this:

models/BTD_model.pth

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🔄 Optional: Auto-Download Script

Use this code to download the CNN model automatically if missing:

import os, urllib.request

model_url = "https://drive.google.com/uc?export=download&id=19SVLCD3DTa1aBZ9PI4TTgNkvJKgL2LSY"
model_path = "models/BTD_model.pth"

if not os.path.exists(model_path):
    os.makedirs("models", exist_ok=True)
    print("Downloading model...")
    urllib.request.urlretrieve(model_url, model_path)
    print("Model downloaded.")

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⚙️ Tech Stack

Category	Tools / Libraries
Language	Python 3.10+
DL Framework	PyTorch
Others	OpenCV, NumPy, scikit-learn, Matplotlib
Training Env	Jupyter Notebook, NVIDIA GPU
Deployment	FastAPI + Vercel (Frontend)
Storage	Google Drive (for model hosting)

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📁 Folder Structure

NeuroAssistAI/
├── main.py                  # Entry point
├── API.py                   # FastAPI backend
├── utils.py                 # Helper functions
├── models/
│   ├── BTD_model.pth
│   └── glioma_stages.pth
├── images/
├── README.md
└── .gitignore

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🚀 How to Run Locally

1️⃣ Clone the Repo

git clone https://github.com/fewgets/NeuroAssistAI.git
cd NeuroAssistAI

2️⃣ Install Dependencies

pip install torch torchvision opencv-python matplotlib scikit-learn fastapi uvicorn

Or use:

pip install -r requirements.txt

3️⃣ Run the App

python main.py

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✨ Features

✅ Tumor classification using CNN

✅ Glioma stage prediction using ANN

✅ Web interface for real-time inference

✅ Pre-trained models included

✅ End-to-end modular system

✅ Easy to extend and deploy

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🔭 Future Enhancements

• 🤖 Chatbot integration for medical Q&A (Gemini/GPT)
• 🧬 Integration with real-time genetic APIs
• 📊 Real-time dashboard for doctors
• 📱 Mobile version using React Native

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📩 Contact

👨‍💻 Usama Shahid 📧 Email: dev.usamashahid@gmail.com 🔗 GitHub: @fewgets

For training notebooks, collab requests, or guidance — feel free to connect.

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⚠️ License

This project is intended for academic and educational purposes only. Re-use is allowed with proper credit and citation. Not intended for clinical deployment without validation.

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🔗 🌐 Live App • 📁 Dataset • 📜 Paper

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