https://github.com/akshint0407/neuroscan-ai

A deep learning-powered diagnostic tool that analyzes MRI scans to detect potential brain tumors using convolutional neural networks (CNNs).
https://github.com/akshint0407/neuroscan-ai

ann cnn-model python streamlit

Last synced: 2 months ago
JSON representation

A deep learning-powered diagnostic tool that analyzes MRI scans to detect potential brain tumors using convolutional neural networks (CNNs).

• Host: GitHub
• URL: https://github.com/akshint0407/neuroscan-ai
• Owner: Akshint0407
• License: apache-2.0
• Created: 2025-04-28T07:49:28.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2025-05-12T06:50:51.000Z (about 1 year ago)
• Last Synced: 2025-07-01T20:51:19.456Z (12 months ago)
• Topics: ann, cnn-model, python, streamlit
• Language: Jupyter Notebook
• Homepage: https://neuroscanai-braintumordetectionfrommriscans.streamlit.app/
• Size: 59.6 KB
• Stars: 0
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

          
# 🧠 NeuroScan AI - Brain Tumor Detection from MRI Scans

![Streamlit App](https://img.shields.io/badge/Streamlit-FF4B4B?style=for-the-badge&logo=Streamlit&logoColor=white)

![TensorFlow](https://img.shields.io/badge/TensorFlow-FF6F00?style=for-the-badge&logo=tensorflow&logoColor=white)

![Python](https://img.shields.io/badge/Python-3776AB?style=for-the-badge&logo=python&logoColor=white)

A deep learning-powered diagnostic tool that analyzes MRI scans to detect potential brain tumors using convolutional neural networks (CNNs).

## ✨ Features

- 🖼️ Upload MRI scans in common image formats (JPG, PNG, JPEG)

- 🔍 AI-powered tumor detection with confidence percentage

- 📊 Visual results with color-coded diagnosis

- 📝 Detailed recommendations based on findings

- 📱 Responsive design works on desktop and mobile

## 🛠️ Installation

1. Clone the repository:

```bash

  https://github.com/Akshint0407/NeuroScan-AI.git

```

```

  cd NeuroScan-AI

```

2. Install dependencies:

```bash

pip install -r requirements.txt

```

3. Run the Streamlit app:

```bash

streamlit run app.py

```

## 🧩 Project Structure

| File/Folder               | Description                          |

|---------------------------|--------------------------------------|

| `app.py`                  | Streamlit application main file      |

| `style.css`               | Custom styling for the web app       |

| `brain_tumor_model.keras` | Trained CNN model for tumor detection|

| `requirements.txt`        | Python dependencies list             |

| `README.md`               | Project documentation                |

## 📂 Dataset

The model was trained on the following datasets:

- Brain Tumor MRI Dataset from kaggle

## 🏗️ Model Architecture

The CNN model consists of:

- 4 Convolutional layers with ReLU activation

- MaxPooling layers for dimensionality reduction

- Dropout layers for regularization

- Dense layers for classification

- Achieved 92.68% validation accuracy on test data.

## 👨‍💻 Developers

- Akshint

- Dhruv

## 📜 License

This project is licensed under the Apache License - see the [LICENSE](LICENSE) file for details.

## 🤝 Contributing

Contributions are welcome! Please open an issue or submit a pull request.

***Disclaimer: This tool is for educational and research purposes only. Always consult a medical professional for clinical diagnosis.***