Diagnosis of Diabetic Retinopathy in Ethiopia: Before the Deep Learning based Automation

2020/03/20 by Misgina Tsighe Hagos, Hagos, Misgina Tsighe
Computer Science · Engineering · Health Professions · Medicine · #Artificial Intelligence in Healthcare #COVID-19 diagnosis using AI #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #Retinal Diseases and Treatments #Retinal Imaging and Analysis #Retinal and Optic Conditions #cs.CV #cs.LG #eess.IV #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2003.09208

openalex publication_date 2020/03/20 · arxiv created 2020/04/29 · arxiv updated 2020/05/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Introducing automated Diabetic Retinopathy (DR) diagnosis into Ethiopia is still a challenging task, despite recent reports that present trained Deep Learning (DL) based DR classifiers surpassing manual graders. This is mainly because of the expensive cost of conventional retinal imaging devices used in DL based classifiers. Current approaches that provide mobile based binary classification of DR, and the way towards a cheaper and offline multi-class classification of DR will be discussed in this paper.

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