Automated Screening for Diabetic Retinopathy Using Compact Deep Networks

Nolan Lunscher, Mei Lin Chen, Ning Jiang, John Zelek

Abstract


Diabetes is a chronic condition affecting millions of people worldwide.
One of its major complications is diabetic retinopathy (DR),
which is the most common cause of legal blindness in the developed
world. Early screening and treatment of DR prevents vision
deterioration, however the recommendation of yearly screening is
often not being met. Mobile screening centres can increasing DR
screening, however they are time and resource intensive because
a clinician is required to process the images. This process can be
improved through computer aided diagnosis, such as by integrating
automated screening on smartphones. Here we explore the use
of a SqueezeNet-based deep network trained on a fundus image
dataset composed of over 88,000 retinal images for the purpose of
computer aided screening for diabetic retinopathy. The results of
this neural network validated the viability of conducting automated
mobile screening of diabetic retinopathy, such as on a smartphone
platform.


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DOI: http://dx.doi.org/10.15353/vsnl.v3i1.182

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