Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/goldsharon/tumor_detection
This Flask-based web app uses a VGG19 model to predict brain tumor types from MRI images. It allows image uploads, performs predictions, and stores patient data in a MySQL database.
https://github.com/goldsharon/tumor_detection
ai-neurological-disorder-detection flask-web-application healthcare mri-image-analysis patient-data-storage-mysql-database vgg19-model-medical-image-processing
Last synced: 25 days ago
JSON representation
This Flask-based web app uses a VGG19 model to predict brain tumor types from MRI images. It allows image uploads, performs predictions, and stores patient data in a MySQL database.
- Host: GitHub
- URL: https://github.com/goldsharon/tumor_detection
- Owner: GoldSharon
- License: mit
- Created: 2024-04-22T14:22:48.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2024-06-09T11:52:11.000Z (7 months ago)
- Last Synced: 2024-06-09T12:55:44.095Z (7 months ago)
- Topics: ai-neurological-disorder-detection, flask-web-application, healthcare, mri-image-analysis, patient-data-storage-mysql-database, vgg19-model-medical-image-processing
- Language: Jupyter Notebook
- Homepage:
- Size: 72.4 MB
- Stars: 0
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Brain Tumor Prediction Web Application
## Overview
This project is a web application developed using Flask to predict brain tumor types based on MRI images. The application utilizes a pre-trained VGG19 model for image classification. It allows users to upload MRI images, processes them through the model, and provides predictions on the type of tumor present in the image. Furthermore, the application stores patient data and their corresponding predicted tumor types in a MySQL database.
## Features
- **Brain Tumor Prediction**: Upload MRI images to predict the type of brain tumor (glioma, meningioma, pituitary, or no tumor).
- **Patient Data Storage**: Save patient names and predicted tumor types in a MySQL database.
- **User-Friendly Interface**: A simple and intuitive web interface for easy interaction.
## Technologies Used
- **Flask**: Web framework used for building the application.
- **TensorFlow**: Machine learning library for model loading and prediction.
- **MySQL**: Database for storing patient data.
- **HTML/CSS**: Front-end design for the web interface.
## Installation
### Prerequisites
- Python 3.x
- Git
- MySQL
### Steps
1. Clone the repository:
```bash
git clone https://github.com/GoldSharon/brain-tumor-prediction.git
```
2. Navigate to the project directory:
```bash
cd brain-tumor-prediction
```
3. Install the required packages:
```bash
pip install -r requirements.txt
```
## Usage
1. Run the Flask application:
```bash
python app.py
```
2. Open your web browser and navigate to `http://127.0.0.1:5000/`.
3. Upload an MRI image and enter the patient's name to predict the brain tumor type.
## Database Configuration
The application uses a MySQL database to store patient data. Update the MySQL connection settings in the `app.py` file before running the application:
```python
db = pymysql.connect(host='localhost',
user='root',
password='your-password',
database='patient_list')