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Diabetic Retinopathy detection through integration of Deep Learning classification framework

View ORCID ProfileAlexander Rakhlin
doi: https://doi.org/10.1101/225508
Alexander Rakhlin
Neuromation OU Tallinn, 10111 Estonia
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Abstract

This document represents a brief account of ongoing project for Diabetic Retinopathy Detection (DRD) through integration of state-of the art Deep Learning methods. We make use of deep Convolutional Neural Networks (CNNs), which have proven revolutionary in multiple fields of computer vision including medical imaging, and we bring their power to the diagnosis of eye fundus images. For training our models we used publicly available Kaggle data set. For testing we used portion of Kaggle data withheld from training and Messidor-2 reference standard. Neither withheld Kaggle images, nor Messidor-2 were used for training. For Messidor-2 we achieved sensitivity 99%, specificity 71%, and AUC 0.97. These results close to recent state-of-the-art models trained on much larger data sets and surpass average results of diabetic retinopathy screening when performed by trained optometrists. With continuous development of our Deep Learning models we expect to further increase the accuracy of the method and expand it to cataract and glaucoma diagnostics.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted June 19, 2018.
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Diabetic Retinopathy detection through integration of Deep Learning classification framework
Alexander Rakhlin
bioRxiv 225508; doi: https://doi.org/10.1101/225508
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Diabetic Retinopathy detection through integration of Deep Learning classification framework
Alexander Rakhlin
bioRxiv 225508; doi: https://doi.org/10.1101/225508

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