Systematic Development of AI-Enabled Diagnostic Systems for Glaucoma and Diabetic Retinopathy
Author:
Affiliation:
1. Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, P. O. Box 51178, Riyadh, Saudi Arabia
Funder
King Saud University, Riyadh, Saudi Arabia
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/6287639/10005208/10256175.pdf?arnumber=10256175
Reference51 articles.
1. Diagnosis of Diabetic Retinopathy Using Deep Neural Networks
2. Retinal Arteriolar Tortuosity and Cardiovascular Risk Factors in a Multi-Ethnic Population Study of 10-Year-Old Children; the Child Heart and Health Study in England (CHASE)
3. Detection and classification of retinal lesions for grading of diabetic retinopathy
4. Automated detection of diabetic retinopathy using SVM
5. FEEDBACK ON A PUBLICLY DISTRIBUTED IMAGE DATABASE: THE MESSIDOR DATABASE
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4. MDLGO: Integrated Multimodal Deep Learning Framework for Enhanced Precision in Glaucoma Diagnosis Using OCT A-scans;2024 International Conference on Emerging Smart Computing and Informatics (ESCI);2024-03-05
5. Refined Anam-Net: Lightweight Deep Learning Model for Improved Segmentation Performance of Optic Cup and Disc for Glaucoma Diagnosis;Computers, Materials & Continua;2024
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