Website URL: https://retinacare.vercel.app/
Able to achieve a stunning accuracy of 83% With approx 3662 imagesall thnx our Image preprocessing Algorithm.
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.
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.
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- Non-invasive, accessible eye health assessment.
- Timely intervention to reduce vision loss risk.
- Aiding both patients and healthcare professionals.
We're motivated by the desire to improve public health, especially for those with diabetes, and to combat vision loss due to diabetic retinopathy.
- Frontend: HTML, CSS, JavaScript
- Backend: Python, Flask
- Machine Learning: Inception ResNet V2, PyTorch, Scikit-Learn, TensorFlow
- Deployment: Docker, Heroku, Vercel
If you have any questions or feedback about RetinaCare, reach me out on linkedIn.
- Shivam Gupta
- Rahul Sharma
Please reach out to the authors for questions or contributions.