Diabetic Retinopathy Detection System
Business Problem
Manual screening of diabetic retinopathy is time-consuming, expertise-dependent, and often delayed—leading to preventable vision loss; there is a critical need to automate and accelerate detection across all severity levels using AI-driven retinal image analysis.
Objectives
- Maximize : early and accurate detection of diabetic retinopathy through AI-driven retinal image classification.
Constraints
- Minimize : clinical expertise dependency, data imbalance challenges, and overfitting risks in severe/proliferative classes.
Success Criteria
1. Business Success Criteria
Maximize screening efficiency and healthcare reach while minimizing diagnostic delays and manual workload.
2. ML Success Criteria
Maximize classification accuracy across all DR severity stages while minimizing bias, misclassification, and training loss.
3. Economic Success Criteria
Maximize affordability and scalability for large-scale screening while minimizing infrastructure and labor costs.