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

S. Majumder; Y. Elloumi; M. Akil; R. Kachouri; N. Kehtarnavaz

doi:10.1117/12.2557554

22 April 2020 A deep learning-based smartphone app for real-time detection of five stages of diabetic retinopathy

S. Majumder, Y. Elloumi, M. Akil, R. Kachouri, N. Kehtarnavaz

Proceedings Volume 11401, Real-Time Image Processing and Deep Learning 2020; 1140106 (2020) https://doi.org/10.1117/12.2557554
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

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 costeffective 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.

Conference Presentation

Citation Download Citation

S. Majumder, Y. Elloumi, M. Akil, R. Kachouri, and N. Kehtarnavaz "A deep learning-based smartphone app for real-time detection of five stages of diabetic retinopathy", Proc. SPIE 11401, Real-Time Image Processing and Deep Learning 2020, 1140106 (22 April 2020); https://doi.org/10.1117/12.2557554

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