Automated Screening for Diabetic Retinopathy Using Compact Deep Networks

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.