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Abstract #91496 Published in IGR 21-4
A combined convolutional and recurrent neural network for enhanced glaucoma detection
Gheisari S; Shariflou S; Phu J; Kennedy PJ; Agar A; Kalloniatis M; Golzan SMScientific reports 2021; 11: 1945
Glaucoma, a leading cause of blindness, is a multifaceted disease with several patho-physiological features manifesting in single fundus images (e.g., optic nerve cupping) as well as fundus videos (e.g., vascular pulsatility index). Current convolutional neural networks (CNNs) developed to detect glaucoma are all based on spatial features embedded in an image. We developed a combined CNN and recurrent neural network (RNN) that not only extracts the spatial features in a fundus image but also the temporal features embedded in a fundus video (i.e., sequential images). A total of 1810 fundus images and 295 fundus videos were used to train a CNN and a combined CNN and Long Short-Term Memory RNN. The combined CNN/RNN model reached an average F-measure of 96.2% in separating glaucoma from healthy eyes. In contrast, the base CNN model reached an average F-measure of only 79.2%. This proof-of-concept study demonstrates that extracting spatial and temporal features from fundus videos using a combined CNN and RNN, can markedly enhance the accuracy of glaucoma detection.
Vision Science Group, Graduate School of Health, University of Technology Sydney, Sydney, Australia. soheila.gheisari@uts.edu.au.
Full articleClassification:
1.6 Prevention and screening (Part of: 1 General aspects)
6.9.5 Other (Part of: 6 Clinical examination methods > 6.9 Computerized image analysis)
6.30 Other (Part of: 6 Clinical examination methods)
