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19 February 2020 Deep learning for objective OCTA detection of diabetic retinopathy
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Proceedings Volume 11218, Ophthalmic Technologies XXX; 112181P (2020) https://doi.org/10.1117/12.2546586
Event: SPIE BiOS, 2020, San Francisco, California, United States
Abstract
Early detection of diabetic retinopathy (DR) is an essential step to prevent vision losses. This study is the first effort to explore convolutional neural networks (CNNs) for transfer-learning based optical coherence tomography angiography (OCTA) detection and classification of DR. We employed transfer-learning using a pre-trained CNN, VGG16, based on the ImageNet dataset for classification of OCTA images. To prevent overfitting, data augmentation, e.g. rotations, flips, and zooming, and 5-fold cross-validation were implemented. A dataset comprising of 131 OCTA images from 20 control, 17 diabetic patients without DR (NoDR), and 60 nonproliferative DR (NPDR) patients were used for preliminary validation. Best classification performance was achieved with fine-tuning nine layers of the sixteen-layer CNN model.
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
David Le, Minhaj Nur Alam, Jennifer I. Lim, R. V. P. Chan, and Xincheng Yao "Deep learning for objective OCTA detection of diabetic retinopathy", Proc. SPIE 11218, Ophthalmic Technologies XXX, 112181P (19 February 2020); https://doi.org/10.1117/12.2546586
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