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https://github.com/chiggi24/cs4442_final_project

A Flask-based web app for brain tumour classification from MRI scans using pre-trained deep learning models. Supports Glioma, Meningioma, Pituitary, and No Tumor detection with model selection and confidence scoring.
https://github.com/chiggi24/cs4442_final_project

brain-tumour cnn deep-learning flask healthcare-ai image-classification keras medical-imaging mri-classification neuroimaging tensorflow transfer-learning

Last synced: 6 months ago
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A Flask-based web app for brain tumour classification from MRI scans using pre-trained deep learning models. Supports Glioma, Meningioma, Pituitary, and No Tumor detection with model selection and confidence scoring.

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README 

          
# 🧠 NeuroScanAI



NeuroScanAI is a deep learning-based tool for classifying brain MRI scans into one of four categories: **Glioma**, **Meningioma**, **Pituitary**, or **None** (no tumour). It offers both a model training pipeline and an interactive web interface for live predictions.



We evaluate and compare the performance of four popular convolutional neural networks:

**ResNet50**, **VGG16**, **EfficientNetB0**, and **InceptionV3** — each trained and validated using Stratified K-Fold cross-validation.



---



## Live Demo (Google Cloud Run)

> Try the deployed version here: [**NeuroScanAI Demo**](https://neuroscanai-427956346530.us-central1.run.app/)



This deployment is hosted using **Google Cloud Run**, a fully managed, serverless platform that runs containerized applications with automatic scaling. We containerized our Flask app using the `Dockerfile` in the `deployment/` directory, then uploaded it to Cloud Run. Our four trained `.keras` models are stored in **Google Cloud Storage** and loaded dynamically at runtime.



---



## Dataset Info



- **Source**: [Brain Tumor Classification (MRI) Dataset - Kaggle](https://www.kaggle.com/datasets/sartajbhuvaji/brain-tumor-classification-mri)

- **License**: MIT License

- **Provided Format**:

  - Grayscale `.jpg` MRI scans with black backgrounds

  - Pre-split into `Training/` and `Testing/` directories, each containing four subfolders (one per class)

- **Preprocessing**:

  - Images were converted to **RGB** and resized to **224x224** for model input compatibility

  - We **merged all images into a single `data/` folder**, organized by four class folders:

    - `Glioma/`

    - `Meningioma/`

    - `Pituitary/`

    - `None/`

- **Classification Task**: Supervised multi-class classification (4 categories)



---



## Project Structure



The repository is organized into two main components: `deployment/` for the web app, and `training/` for the model development pipeline.



```bash

cs4442_final_project/

│

├── deployment/               # Flask-based frontend + backend for prediction

│   ├── static/               # Static assets (JS, CSS, and tooltip examples)

│   │   ├── images/           # Sample MRI images for each tumour class

│   │   ├── script.js         # Handles image upload, preview, results, tooltips

│   │   └── style.css         # Light/dark theme and component styling

│   │

│   ├── templates/            # HTML templates

│   │   └── index.html        # Main web interface layout

│   │

│   ├── app.py                # Main Flask server application

│   ├── Dockerfile            # Docker config for Cloud Run deployment

│   └── requirements.txt      # Dependencies for deployment environment

│

├── training/                 # All training scripts, data, and notebooks

│   ├── data/                 # Merged dataset with subfolders per class

│   │   ├── Glioma/

│   │   ├── Meningioma/

│   │   ├── None/

│   │   └── Pituitary/

│   │

│   ├── models/               # Saved Keras model files from training

│   │

│   ├── brain_tumor_classification.ipynb   # End-to-end training & analysis notebook

│   ├── config.py                         # Hyperparameters and constants

│   ├── data_augmentation.py              # Functions for augmenting MRI data

│   ├── data_preprocessing.py             # Image preprocessing routines

│   ├── feature_extraction.py             # Model feature extraction functions

│   ├── model_evaluation.py               # Evaluation, visualization, and metrics

│   ├── model_training.py                 # K-fold training logic with saving/loading

│   ├── requirements.txt                  # Dependencies for training pipeline

│   └── utils.py                          # Reusable helper functions for training

│

├── .gitignore                # Standard Git ignore rules

└── README.md                 # Project overview and usage guide



```



---



## Getting Started



Ensure you have **Python 3.11** installed before proceeding.



Clone the repository:



```bash

git clone https://github.com/chiggi24/cs4442_final_project.git

cd cs4442_final_project

```



---



## Running the Training Notebook



> Located in `training/brain_tumor_classification.ipynb`



- Make sure you're inside the cloned `cs4442_final_project` directory.

- Navigate to the `training/` folder:



```bash

cd training

```

- Create and activate a virtual environment using Python 3.11:



```bash

python3.11 -m venv venv

source venv/bin/activate  # or venv\Scripts\activate on Windows

```

- Install the required dependencies:

```bash

pip install -r requirements.txt

```

- Register the environment as a new Jupyter kernel:

```bash

python -m ipykernel install --user --name=neuroscanai --display-name "NeuroScanAI"

```



- Launch Jupyter Notebook and in the top-right dropdown, select the `NeuroScanAI` kernel

- All training code, plots, and evaluation logic are included

- Trained `.keras` models will be saved into the `training/models/` directory



> **Note 1**: To speed up training during testing, you can reduce the number of epochs and K-folds in `training/config.py`:



```python

# Number of training epochs

EPOCHS = 2  



# Number of folds for K-Fold Cross-Validation 

K_FOLDS = 2

```



> **Note 2**: If you prefer to skip training and use pre-existing trained models instead, you can download them from the following [Pre-trained Models (Google Drive)](https://drive.google.com/drive/folders/1qr_dh3GFHygpjIOCY8k6aU1itj16Ydxl). After downloading, place the `.keras` model files into the `training/models/` directory.



---



## Local Deployment (Flask Web App)



> Ensure you're using **Python 3.11** for TensorFlow compatibility.



- From the project root (`cs4442_final_project/`), navigate to the `deployment/` folder:



```bash

cd deployment

```

- Create and activate a virtual environment:

```bash

python3.11 -m venv venv

source venv/bin/activate  # or venv\Scripts\activate on Windows

```

- Install dependencies:

```bash

pip install -r requirements.txt

```

Run the Flask app:

```bash

python app.py 

```

- Open your browser and go to: [http://localhost:8080](http://localhost:8080)



---



## Dependencies



The project maintains **separate environments** for training and deployment, each with its own set of dependencies defined in `requirements.txt`.



### Key libraries:

- **TensorFlow** – Core deep learning framework for model development and inference

- **OpenCV** – Image preprocessing and format handling

- **Matplotlib** & **Seaborn** – Visualization of metrics and results

- **Scikit-learn** – Metrics, preprocessing, and validation utilities

- **Flask** – Lightweight web framework used for deployment

- **Google Cloud Storage** – Access to hosted `.keras` models in production

- **Pillow** – Support for image file operations



---



## Contributors  

- **Christopher Higgins**  

- **Bryson Crook**  

- **Mohamed El Dogdog**