Deep Learning Approach to Diabetic Retinopathy Detection

Borys Tymchenko, Philip Marchenko, Dmitry Spodarets

2020

Abstract

Diabetic retinopathy is one of the most threatening complications of diabetes that leads to permanent blindness if left untreated. One of the essential challenges is early detection, which is very important for treatment success. Unfortunately, the exact identification of the diabetic retinopathy stage is notoriously tricky and requires expert human interpretation of fundus images. Simplification of the detection step is crucial and can help millions of people. Convolutional neural networks (CNN) have been successfully applied in many adjacent subjects, and for diagnosis of diabetic retinopathy itself. However, the high cost of big labeled datasets, as well as inconsistency between different doctors, impede the performance of these methods. In this paper, we propose an automatic deep-learning-based method for stage detection of diabetic retinopathy by single photography of the human fundus. Additionally, we propose the multistage approach to transfer learning, which makes use of similar datasets with different labeling. The presented method can be used as a screening method for early detection of diabetic retinopathy with sensitivity and specificity of 0.99 and is ranked 54 of 2943 competing methods (quadratic weighted kappa score of 0.925466) on APTOS 2019 Blindness Detection Dataset (13000 images).

Paper Citation

in Harvard Style

Tymchenko B., Marchenko P. and Spodarets D. (2020). Deep Learning Approach to Diabetic Retinopathy Detection. In Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-397-1, pages 501-509. DOI: 10.5220/0008970805010509

in Bibtex Style

@conference{icpram20,
author={Borys Tymchenko and Philip Marchenko and Dmitry Spodarets},
title={Deep Learning Approach to Diabetic Retinopathy Detection},
booktitle={Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2020},
pages={501-509},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008970805010509},
isbn={978-989-758-397-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Deep Learning Approach to Diabetic Retinopathy Detection
SN - 978-989-758-397-1
AU - Tymchenko B.
AU - Marchenko P.
AU - Spodarets D.
PY - 2020
SP - 501
EP - 509
DO - 10.5220/0008970805010509