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A deep learning-based smartphone app for real-time detection of five stages of diabetic retinopathy

Abstract : This paper presents the real-time implementation of deep neural networks on smartphone platforms to detect and classify diabetic retinopathy from eye fundus images. This implementation is an extension of a previously reported implementation by considering all the five stages of diabetic retinopathy. Two deep neural networks are first trained, one for detecting four stages and the other to further classify the last stage into two more stages, based on the EyePACS and APTOS datasets fundus images and by using transfer learning. Then, it is shown how these trained networks are turned into a smartphone app, both Android and iOS versions, to process images captured by smartphone cameras in real-time. The app is designed in such a way that fundus images can be captured and processed in real-time by smartphones together with lens attachments that are commercially available. The developed real-time smartphone app provides a cost-effective and widely accessible approach for conducting first-pass diabetic retinopathy eye exams in remote clinics or areas with limited access to fundus cameras and ophthalmologists. Keywords: Real-time implementation of deep neural networks on smartphones, real-time smartphone app for detection and classification of diabetic retinopathy, first-pass eye exam by smartphone app.
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Submitted on : Tuesday, April 28, 2020 - 1:27:51 PM
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Sharmin Majumder, Yaroub Elloumi, Mohamed Akil, Rostom Kachouri, Nasser Kehtarnavaz. A deep learning-based smartphone app for real-time detection of five stages of diabetic retinopathy. Real-Time Image Processing and Deep Learning 2020, Apr 2020, San Diego, California (Online Only), United States. pp.5, ⟨10.1117/12.2557554⟩. ⟨hal-02556991⟩



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