Diagnosis of Diabetic Retinopathy in Ethiopia: Before the Deep Learning based Automation
This work tackles the problem of making DR diagnosis accessible in low-resource settings like Ethiopia, but it appears incremental as it builds on existing mobile and classification methods.
The paper addresses the challenge of implementing automated Diabetic Retinopathy diagnosis in Ethiopia due to high costs of conventional retinal imaging devices, and discusses mobile-based binary classification and cheaper offline multi-class classification approaches.
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.