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https://github.com/shivamgupta92/retinacare-early-detection-of-diabetic-retinopathy
Early Detection of Diabetic Retinopathy System, a Flask-based web app, uses machine learning to assess diabetic retinopathy risk. Input your health data and get results within seconds: Ranging from ['Mild', 'Moderate', 'Severe', 'No_DR']
https://github.com/shivamgupta92/retinacare-early-detection-of-diabetic-retinopathy
cnn-keras css diabetic-retinopathy-detection flask html5 inception-resnet-v2 javascript tensorflow
Last synced: 1 day ago
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Early Detection of Diabetic Retinopathy System, a Flask-based web app, uses machine learning to assess diabetic retinopathy risk. Input your health data and get results within seconds: Ranging from ['Mild', 'Moderate', 'Severe', 'No_DR']
- Host: GitHub
- URL: https://github.com/shivamgupta92/retinacare-early-detection-of-diabetic-retinopathy
- Owner: ShivamGupta92
- License: mit
- Created: 2023-10-21T14:25:15.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-09-09T13:18:15.000Z (2 months ago)
- Last Synced: 2024-09-09T16:03:40.897Z (2 months ago)
- Topics: cnn-keras, css, diabetic-retinopathy-detection, flask, html5, inception-resnet-v2, javascript, tensorflow
- Language: Jupyter Notebook
- Homepage: https://retinacare.vercel.app
- Size: 19.1 MB
- Stars: 9
- Watchers: 2
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
 
RetinaCare
Website URL: https://retinacare.vercel.app/
# Early Detection of Diabetic Retinopathy
# Accuracy
```sh
Able to achieve a stunning accuracy of 93% With approx 3662 images
```
# About
Diabetic Retinopathy Detection, or RetinaCare, is an AI-driven solution aimed at identifying diabetic retinopathy, a critical complication of diabetes, using retinal images. Our project offers a non-invasive, accessible, and efficient tool to assess the severity of this condition.
# Problem We Solve
Diabetic Retinopathy is a disease with an increasing prevalence and the main cause of blindness among working-age population. The risk of severe vision loss can be significantly reduced by timely diagnosis and treatment. Systematic screening for DR has been identified as a cost-effective way to save health services resources. Automatic retinal image analysis is emerging as an important screening tool for early DR detection, which can reduce the workload associated to manual grading as well as save diagnosis costs and time. Many research efforts in the last years have been devoted to developing automated tools to help in the detection and evaluation of DR lesions. We are interested in automating this predition using deep learning models.
**Why Use Our Model:**
Our model provides:
- Quick, accurate diagnosis of retinopathy.
- Early intervention for effective treatment.
- Personalized recommendations based on severity.
# Data Visualization
# Final segmentation output
# Model Working
https://github.com/ShivamGupta92/Retinacare/assets/70855458/0ebcc8e0-45cc-4415-9a5b-5d44b820b035
## How It Addresses Real-World Problems
- Non-invasive, accessible eye health assessment.
- Timely intervention to reduce vision loss risk.
- Aiding both patients and healthcare professionals.
## Motivation:
We're motivated by the desire to improve public health, especially for those with diabetes, and to combat vision loss due to diabetic retinopathy.
## Tech Stack:
- Frontend: HTML, CSS, JavaScript
- Backend: Python, Flask
- Machine Learning: Inception ResNet V2, PyTorch, Scikit-Learn, TensorFlow
- Deployment: Vercel
## Write Us
If you have any questions or feedback about RetinaCare, reach me out on linkedIn.
## Author
- [Shivam Gupta](https://www.linkedin.com/in/shivam-gupta-453b13217)
Please reach out to the authors for questions or contributions.