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. 2017 Aug 25;7:9425. doi: 10.1038/s41598-017-09891-x

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© The Author(s) 2017

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Overall architecture of the model. The data set for the retinal fundus images (96 × 96 pixels) is labelled as Input. Each of the convolutional layers (Conv1–3) is followed by an activation function (ReLU) layer, pooling layers (MP1–3) and two fully connected layers (FC1, FC2). The final output layer performs binary classification by using a softmax function.