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?curacy, 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.
Rumpun Ilmu
Ilmu Komputer
Bahasa Asli/Original Language
English
Level
Nasional
Status
Dokumen Karya
No
Judul
Tipe Dokumen
Aksi
1
8014-28760-1-PB (3).pdf
[PAK] Full Dokumen
2
SK Akreditasi Jurnal Ilmiah Periode I Tahun 2025_pdf.pdf
Dokumen Pendukung Karya Ilmiah (Hibah, Publikasi, Penelitian, Pengabdian)
3
PAK-Bukti Korespondensi JPIT Scanocular.pdf
[PAK] Bukti Korespondensi Penulis
4
[Turnitin] SCANOCULAR- Application for Early Detection of Eye Diseases Using AI and Blockchain Technology.pdf
