Deep Learning Fundus Image Analysis for Diabetic Retinopathy and Macular Edema Grading

Tutkimustuotos: Lehtiartikkeli

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Deep Learning Fundus Image Analysis for Diabetic Retinopathy and Macular Edema Grading. / Sahlsten, Jaakko; Jaskari, Joel; Kivinen, Jyri; Turunen, Lauri; Jaanio, Esa; Hietala, Kustaa; Kaski, Kimmo.

julkaisussa: Scientific Reports, Vuosikerta 9, Nro 1, 10750, 01.12.2019, s. 1-11.

Tutkimustuotos: Lehtiartikkeli

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Bibtex - Lataa

@article{2274283c754b4fad953f81386a5be812,
title = "Deep Learning Fundus Image Analysis for Diabetic Retinopathy and Macular Edema Grading",
abstract = "Diabetes is a globally prevalent disease that can cause visible microvascular complications such as diabetic retinopathy and macular edema in the human eye retina, the images of which are today used for manual disease screening and diagnosis. This labor-intensive task could greatly benefit from automatic detection using deep learning technique. Here we present a deep learning system that identifies referable diabetic retinopathy comparably or better than presented in the previous studies, although we use only a small fraction of images (<1/4) in training but are aided with higher image resolutions. We also provide novel results for five different screening and clinical grading systems for diabetic retinopathy and macular edema classification, including state-of-the-art results for accurately classifying images according to clinical five-grade diabetic retinopathy and for the first time for the four-grade diabetic macular edema scales. These results suggest, that a deep learning system could increase the cost-effectiveness of screening and diagnosis, while attaining higher than recommended performance, and that the system could be applied in clinical examinations requiring finer grading.",
author = "Jaakko Sahlsten and Joel Jaskari and Jyri Kivinen and Lauri Turunen and Esa Jaanio and Kustaa Hietala and Kimmo Kaski",
year = "2019",
month = "12",
day = "1",
doi = "10.1038/s41598-019-47181-w",
language = "English",
volume = "9",
pages = "1--11",
journal = "Scientific Reports",
issn = "2045-2322",
publisher = "Nature Publishing Group",
number = "1",

}

RIS - Lataa

TY - JOUR

T1 - Deep Learning Fundus Image Analysis for Diabetic Retinopathy and Macular Edema Grading

AU - Sahlsten, Jaakko

AU - Jaskari, Joel

AU - Kivinen, Jyri

AU - Turunen, Lauri

AU - Jaanio, Esa

AU - Hietala, Kustaa

AU - Kaski, Kimmo

PY - 2019/12/1

Y1 - 2019/12/1

N2 - Diabetes is a globally prevalent disease that can cause visible microvascular complications such as diabetic retinopathy and macular edema in the human eye retina, the images of which are today used for manual disease screening and diagnosis. This labor-intensive task could greatly benefit from automatic detection using deep learning technique. Here we present a deep learning system that identifies referable diabetic retinopathy comparably or better than presented in the previous studies, although we use only a small fraction of images (<1/4) in training but are aided with higher image resolutions. We also provide novel results for five different screening and clinical grading systems for diabetic retinopathy and macular edema classification, including state-of-the-art results for accurately classifying images according to clinical five-grade diabetic retinopathy and for the first time for the four-grade diabetic macular edema scales. These results suggest, that a deep learning system could increase the cost-effectiveness of screening and diagnosis, while attaining higher than recommended performance, and that the system could be applied in clinical examinations requiring finer grading.

AB - Diabetes is a globally prevalent disease that can cause visible microvascular complications such as diabetic retinopathy and macular edema in the human eye retina, the images of which are today used for manual disease screening and diagnosis. This labor-intensive task could greatly benefit from automatic detection using deep learning technique. Here we present a deep learning system that identifies referable diabetic retinopathy comparably or better than presented in the previous studies, although we use only a small fraction of images (<1/4) in training but are aided with higher image resolutions. We also provide novel results for five different screening and clinical grading systems for diabetic retinopathy and macular edema classification, including state-of-the-art results for accurately classifying images according to clinical five-grade diabetic retinopathy and for the first time for the four-grade diabetic macular edema scales. These results suggest, that a deep learning system could increase the cost-effectiveness of screening and diagnosis, while attaining higher than recommended performance, and that the system could be applied in clinical examinations requiring finer grading.

UR - http://www.scopus.com/inward/record.url?scp=85069691697&partnerID=8YFLogxK

U2 - 10.1038/s41598-019-47181-w

DO - 10.1038/s41598-019-47181-w

M3 - Article

VL - 9

SP - 1

EP - 11

JO - Scientific Reports

JF - Scientific Reports

SN - 2045-2322

IS - 1

M1 - 10750

ER -

ID: 36027452