SCANOCULAR: Application for Early Detection of Eye Diseases Using AI and Blockchain Technology

Abstrak

Eye diseases such as cataracts, glaucoma, and diabetic retinopathy affect approximately 2.2 billion people globally, with 1 billion cases being preventable. In Indonesia, cataracts remain the leading cause of blindness. This research presents SCANOCULAR, a mobile application that integrates artificial intelligence (AI) and blockchain technology for early detection of eye diseases. The system utilizes a modified EfficientNetB4 Convolutional Neural Network (CNN) for analyzing eye images, achieving 95.50% accuracy, 95.92% precision, and 94.95% recall in cataract detection with an AUC of 0.9932. The blockchain implementation using Polygon Amoy platform ensures secure data transmission and storage while maintaining efficient transaction processing. Testing results demonstrate the system's capability in identifying various eye conditions while maintaining data integrity through blockchain verification. SCANOCULAR contributes to informatics by implementing a hybrid AI-blockchain architecture optimized for medical imaging applications, with a lightweight CNN model design that reduces computational requirements while maintaining diagnostic accuracy. This integration of technologies provides a potential solution for improving accessibility to eye disease screening and early intervention in Indonesia.