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: 8 months ago
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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 (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2025-06-17T10:49:28.000Z (about 1 year ago)
- Last Synced: 2025-06-17T11:45:21.447Z (about 1 year ago)
- Topics: classification, deep-learning, deep-neural-networks, keras, python, tensorflow
- Language: Python
- Homepage:
- Size: 4.43 MB
- Stars: 5
- Watchers: 1
- 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).