https://github.com/jpedrou/brain-tumor-detection

This project uses deep learning with CNNs to detect brain tumors in MRI images, aiming for accurate tumor pattern identification.
https://github.com/jpedrou/brain-tumor-detection

classification deep-learning deep-neural-networks keras python tensorflow

Last synced: 2 days ago
JSON representation

This project uses deep learning with CNNs to detect brain tumors in MRI images, aiming for accurate tumor pattern identification.

• Host: GitHub
• URL: https://github.com/jpedrou/brain-tumor-detection
• Owner: jpedrou
• License: mit
• Created: 2024-06-30T12:33:51.000Z (8 months ago)
• Default Branch: main
• Last Pushed: 2024-11-27T22:47:14.000Z (3 months ago)
• Last Synced: 2024-11-27T23:27:09.636Z (3 months ago)
• Topics: classification, deep-learning, deep-neural-networks, keras, python, tensorflow
• Language: Python
• Homepage:
• Size: 4.42 MB
• Stars: 4
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        


Brain Tumor Detection


This repository aims to implement a Convolutional Neural Network (CNN) for brain tumor detection. The project includes a Python-based CNN model trained on a dataset of brain MRI scans to classify images as either tumor-present or tumor-absent. Additionally, a user-friendly interface is provided to allow easy interaction with the trained model. This interface enables users to upload MRI scans and visualize predictions. The data  is from https://www.kaggle.com/datasets/ahmedhamada0/brain-tumor-detection. 

The project serves as a practical demonstration of applying deep learning techniques to medical image analysis, specifically for brain tumor detection, with accessibility in mind through a user-friendly interface.

**Goal**

- Implement a CNN for brain tumor detection from MRI images.

- Integrate a simple and intuitive user interface for interaction.

## Installation

1. Install dependencies:

```bash

conda env create -f environment.yml

```

2. In the directory where is the file app.py, run:

```bash

python app.py

```

3. Open the index.html in your browser (located in templates folder).