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Proceedings Paper

A deep learning-based smartphone app for real-time detection of retinal abnormalities in fundus images
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Paper Abstract

This paper presents the real-time implementation of two deep neural networks, which are trained for detection of eye retina abnormalities, on smartphones as an app. This app provides a low-cost and universally accessible alternative to fundus cameras since smartphones are widely available and they can be fitted with lenses that are commercially available for examination of the retina. The process of training two convolutional neural networks for retinal abnormality detection based on two publicly available datasets is discussed. Furthermore, it is shown how a smartphone app, both Android and iOS versions, are created from these trained networks. The results obtained indicate that it is possible to carry out the detection of retinal abnormalities on smartphones in an on-the-fly manner as retina images get captured by their cameras in real-time.

Paper Details

Date Published: 14 May 2019
PDF: 8 pages
Proc. SPIE 10996, Real-Time Image Processing and Deep Learning 2019, 1099602 (14 May 2019); doi: 10.1117/12.2516665
Show Author Affiliations
Haoran Wei, The Univ. of Texas at Dallas (United States)
Abhishek Sehgal, The Univ. of Texas at Dallas (United States)
Nasser Kehtarnavaz, The Univ. of Texas at Dallas (United States)


Published in SPIE Proceedings Vol. 10996:
Real-Time Image Processing and Deep Learning 2019
Nasser Kehtarnavaz; Matthias F. Carlsohn, Editor(s)

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