Deep learning generative adversarial network model for automated detection of diabetic retinopathy

What is it about?

Due to the obvious large number of DR patients and limited medical resources in particular areas, patients with DR may not be treated in time, therefore missing out the best treatment options and eventually leading to irreversible vision loss. Unfortunately, a manual diagnosis to examine DR is tedious, time consuming, and error-prone, besides the consequences of manual interpretation which is highly dependent on the medical expert experiences to identify the presence of small features and significance of DR. This manual method opens to the inconsistency of the diagnosis. Thus, Automated Diabetic Retinopathy Detection aims to reduce the burden on ophthalmologists and mitigate diagnostic inconsistencies between manual readers by classifying DR stages using previous DR images with stages labels using Deep Learning.

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Photo by Jeswin Thomas on Unsplash

Photo by Jeswin Thomas on Unsplash