https://github.com/gaoux/octsense
https://github.com/gaoux/octsense
oct react vite-react
Last synced: about 2 months ago
JSON representation
- Host: GitHub
- URL: https://github.com/gaoux/octsense
- Owner: Gaoux
- License: gpl-3.0
- Created: 2025-02-07T00:35:22.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-05-31T16:56:18.000Z (about 1 year ago)
- Last Synced: 2025-06-19T10:43:03.891Z (12 months ago)
- Topics: oct, react, vite-react
- Language: JavaScript
- Homepage: https://oct-sense.vercel.app
- Size: 831 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# OCTsense
OCTsense is a **web platform (SPA)** for the **automated analysis of OCT (Optical Coherence Tomography) images** using **artificial intelligence**.
It is designed for **ophthalmologists** who need support diagnosing ocular diseases such as **Choroidal Neovascularization (CNV)**, **Age-Related Macular Degeneration (AMD)**, macular edema, and others — without relying on image interpretation experts.
OCTsense also serves **patients and ophthalmologists** by automatically detecting:
- Choroidal Neovascularization (CNV)
- Diabetic Macular Edema (DME)
- Drusen lesions (DRUSEN)
- Healthy retinal tissue
—all without depending on expert image analysis.
---
## 🔗 Related Repositories
- **Backend (Django + REST API)**
[https://github.com/gaoux/OCT-diagnosis-backend](https://github.com/Gaoux/OCT-diagnosis-backend)
- **AI Model (Hugging Face – OCT Classification)**
[https://huggingface.co/gaoux/OCT_class](https://huggingface.co/gaoux/OCT_class)
---
## 🚀 Technologies Used
| Area | Technology |
| :------- | :-------------------------------------- |
| Frontend | React.js (SPA) with Vite |
| Backend | Django + Django REST Framework (Python) |
| AI Model | TensorFlow 2.x, Keras, OpenCV |
| Database | PostgreSQL |
---
## 🧹 Main Functional Modules
- **User and authentication management:**
Registration for ophthalmologists and admins, credential validation, password recovery, role control.
- **Landing Page:**
Interactive welcome screen with guides and access to main features.
- **AI-driven image analysis:**
Pretrained TensorFlow models process images to generate preliminary diagnostic predictions.
- **Results and reports:**
View analysis results, generate medical reports in PDF, download/store reports, and compare historical images.
---
# ⚙️ Setup Guide
Follow these steps to install and run the project locally:
## 1. Clone the repository
```bash
git clone https://your-repository-url.git
cd your-repository-folder
```
---
## 2. Backend (Django + PostgreSQL)
The backend code for OCTsense is located in a separate repository. You can find it here:
**Backend Repository:** [OCTsense Backend](https://your-backend-repository-link)
Follow the instructions in that repository to set up and run the backend server locally.
---
## 3. Frontend (React + Vite)
### a. Environment Variables
Create a `.env` file in the frontend folder:
```env
VITE_API_BASE_URL=http://127.0.0.1:8000/
```
---
### b. Run Frontend with Docker
```bash
docker-compose up --build
```
Frontend will be available at:
```
http://localhost:3000
```
---
# 📈 Quick Commands Summary
| Task | Command |
| :--------------------- | :----------------------------------------------- |
| Build and run backend | Refer to backend repository instructions |
| Build and run frontend | `docker-compose up --build` (in frontend folder) |
| Access backend API | http://127.0.0.1:8000/api/ |
| Access frontend app | http://localhost:3000 |
---
# ✨ Extra Notes
- AI models are managed separately inside the backend (`oct/predict/` endpoint).
- Make sure ports **8000 (backend)** and **3000 (frontend)** are open.
- For production, it's recommended to serve the frontend using **Nginx** (already set up).
---
# 💬 License
This project is licensed under the GPL-3.0 license.
---
# 📬 Contact
For questions, contributions, or collaboration inquiries:
**Gustavo Parra | parrat-ga@javeriana.edu.co**
---