Early detection of retinal diseases like diabetic retinopathy can prevent blindness in millions of patients worldwide. Yet access to specialist ophthalmologists remains limited, especially in developing countries. That's the problem we set out to solve with OptiPro.
- What is OptiPro?
OptiPro is an AI-powered retinal disease detection system that analyzes fundus images and classifies retinal conditions with high accuracy giving clinicians a fast, reliable second opinion.
- How It Works
The core model is a convolutional neural network (CNN) trained on labeled fundus image datasets. The pipeline looks like this:
- Image ingestion — fundus photos uploaded via web interface
- Preprocessing — resizing, normalization, contrast enhancement
- Inference — CNN classifies the image across multiple disease categories
- Result — confidence score + condition label returned to the clinician
- Tech Stack
- Model: Python, TensorFlow, OpenCV
- Backend: FastAPI
- Frontend: Next.js
- Deployment: Docker
- What We Learned
Training on imbalanced medical datasets is hard. We used weighted loss functions and aggressive augmentation (flips, rotations, brightness shifts) to prevent the model from overfitting to the majority class.
- What's Next
OptiPro is currently in beta. We're working on expanding the disease categories and integrating it into clinical workflows.
We build projects like this at https://www.hydrabytes.tech - a web, mobile, and AI development agency based in Islamabad. If you're working on something ambitious, let's talk.
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