Detection of diabetic retinopathy based on a convolutional neural network using retinal fundus images

Gabriel García; Jhair Gallardo; Antoni Mauricio; Jorge López; Christian Del Carpio

doi:10.1007/978-3-319-68612-7_72

Detection of diabetic retinopathy based on a convolutional neural network using retinal fundus images

Gabriel García
, Jhair Gallardo
, Antoni Mauricio
, Jorge López
, Christian Del Carpio

Universidad Nacional de Ingeniería, Peru
Universidad Católica de San Pablo

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

92 Scopus citations

Abstract

Diabetic retinopathy is one of the leading causes of blindness. Its damage is associated with the deterioration of blood vessels in retina. Progression of visual impairment may be cushioned or prevented if detected early, but diabetic retinopathy does not present symptoms prior to progressive loss of vision, and its late detection results in irreversible damages. Manual diagnosis is performed on retinal fundus images and requires experienced clinicians to detect and quantify the importance of several small details which makes this an exhaustive and time-consuming task. In this work, we attempt to develop a computer-assisted tool to classify medical images of the retina in order to diagnose diabetic retinopathy quickly and accurately. A neural network, with CNN architecture, identifies exudates, micro-aneurysms and hemorrhages in the retina image, by training with labeled samples provided by EyePACS, a free platform for retinopathy detection. The database consists of 35126 high-resolution retinal images taken under a variety of conditions. After training, the network shows a specificity of 93.65% and an accuracy of 83.68% on validation process.

Original language	English
Title of host publication	Artificial Neural Networks and Machine Learning – ICANN 2017 - 26th International Conference on Artificial Neural Networks, Proceedings
Editors	Alessandra Lintas, Alessandro E. Villa, Stefano Rovetta, Paul F. Verschure
Publisher	Springer Verlag
Pages	635-642
Number of pages	8
ISBN (Print)	9783319686110
DOIs	https://doi.org/10.1007/978-3-319-68612-7_72
State	Published - 2017
Externally published	Yes
Event	26th International Conference on Artificial Neural Networks, ICANN 2017 - Alghero, Italy Duration: 11 Sep 2017 → 14 Sep 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10614 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	26th International Conference on Artificial Neural Networks, ICANN 2017
Country/Territory	Italy
City	Alghero
Period	11/09/17 → 14/09/17

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Convolutional neural network
Deep learning
Diabetic retinopathy
Medical image classification

Access to Document

10.1007/978-3-319-68612-7_72

Cite this

García, G., Gallardo, J., Mauricio, A., López, J., & Del Carpio, C. (2017). Detection of diabetic retinopathy based on a convolutional neural network using retinal fundus images. In A. Lintas, A. E. Villa, S. Rovetta, & P. F. Verschure (Eds.), Artificial Neural Networks and Machine Learning – ICANN 2017 - 26th International Conference on Artificial Neural Networks, Proceedings (pp. 635-642). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10614 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-68612-7_72

García, Gabriel ; Gallardo, Jhair ; Mauricio, Antoni et al. / Detection of diabetic retinopathy based on a convolutional neural network using retinal fundus images. Artificial Neural Networks and Machine Learning – ICANN 2017 - 26th International Conference on Artificial Neural Networks, Proceedings. editor / Alessandra Lintas ; Alessandro E. Villa ; Stefano Rovetta ; Paul F. Verschure. Springer Verlag, 2017. pp. 635-642 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{b0a84cff9b744437a99689bd3822c355,

title = "Detection of diabetic retinopathy based on a convolutional neural network using retinal fundus images",

abstract = "Diabetic retinopathy is one of the leading causes of blindness. Its damage is associated with the deterioration of blood vessels in retina. Progression of visual impairment may be cushioned or prevented if detected early, but diabetic retinopathy does not present symptoms prior to progressive loss of vision, and its late detection results in irreversible damages. Manual diagnosis is performed on retinal fundus images and requires experienced clinicians to detect and quantify the importance of several small details which makes this an exhaustive and time-consuming task. In this work, we attempt to develop a computer-assisted tool to classify medical images of the retina in order to diagnose diabetic retinopathy quickly and accurately. A neural network, with CNN architecture, identifies exudates, micro-aneurysms and hemorrhages in the retina image, by training with labeled samples provided by EyePACS, a free platform for retinopathy detection. The database consists of 35126 high-resolution retinal images taken under a variety of conditions. After training, the network shows a specificity of 93.65\% and an accuracy of 83.68\% on validation process.",

keywords = "Convolutional neural network, Deep learning, Diabetic retinopathy, Medical image classification",

author = "Gabriel Garc{\'i}a and Jhair Gallardo and Antoni Mauricio and Jorge L{\'o}pez and \{Del Carpio\}, Christian",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 26th International Conference on Artificial Neural Networks, ICANN 2017 ; Conference date: 11-09-2017 Through 14-09-2017",

year = "2017",

doi = "10.1007/978-3-319-68612-7\_72",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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booktitle = "Artificial Neural Networks and Machine Learning – ICANN 2017 - 26th International Conference on Artificial Neural Networks, Proceedings",

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García, G, Gallardo, J, Mauricio, A, López, J & Del Carpio, C 2017, Detection of diabetic retinopathy based on a convolutional neural network using retinal fundus images. in A Lintas, AE Villa, S Rovetta & PF Verschure (eds), Artificial Neural Networks and Machine Learning – ICANN 2017 - 26th International Conference on Artificial Neural Networks, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10614 LNCS, Springer Verlag, pp. 635-642, 26th International Conference on Artificial Neural Networks, ICANN 2017, Alghero, Italy, 11/09/17. https://doi.org/10.1007/978-3-319-68612-7_72

Detection of diabetic retinopathy based on a convolutional neural network using retinal fundus images. / García, Gabriel; Gallardo, Jhair; Mauricio, Antoni et al.
Artificial Neural Networks and Machine Learning – ICANN 2017 - 26th International Conference on Artificial Neural Networks, Proceedings. ed. / Alessandra Lintas; Alessandro E. Villa; Stefano Rovetta; Paul F. Verschure. Springer Verlag, 2017. p. 635-642 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10614 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Detection of diabetic retinopathy based on a convolutional neural network using retinal fundus images

AU - García, Gabriel

AU - Gallardo, Jhair

AU - Mauricio, Antoni

AU - López, Jorge

AU - Del Carpio, Christian

N1 - Publisher Copyright: © Springer International Publishing AG 2017.

PY - 2017

Y1 - 2017

N2 - Diabetic retinopathy is one of the leading causes of blindness. Its damage is associated with the deterioration of blood vessels in retina. Progression of visual impairment may be cushioned or prevented if detected early, but diabetic retinopathy does not present symptoms prior to progressive loss of vision, and its late detection results in irreversible damages. Manual diagnosis is performed on retinal fundus images and requires experienced clinicians to detect and quantify the importance of several small details which makes this an exhaustive and time-consuming task. In this work, we attempt to develop a computer-assisted tool to classify medical images of the retina in order to diagnose diabetic retinopathy quickly and accurately. A neural network, with CNN architecture, identifies exudates, micro-aneurysms and hemorrhages in the retina image, by training with labeled samples provided by EyePACS, a free platform for retinopathy detection. The database consists of 35126 high-resolution retinal images taken under a variety of conditions. After training, the network shows a specificity of 93.65% and an accuracy of 83.68% on validation process.

AB - Diabetic retinopathy is one of the leading causes of blindness. Its damage is associated with the deterioration of blood vessels in retina. Progression of visual impairment may be cushioned or prevented if detected early, but diabetic retinopathy does not present symptoms prior to progressive loss of vision, and its late detection results in irreversible damages. Manual diagnosis is performed on retinal fundus images and requires experienced clinicians to detect and quantify the importance of several small details which makes this an exhaustive and time-consuming task. In this work, we attempt to develop a computer-assisted tool to classify medical images of the retina in order to diagnose diabetic retinopathy quickly and accurately. A neural network, with CNN architecture, identifies exudates, micro-aneurysms and hemorrhages in the retina image, by training with labeled samples provided by EyePACS, a free platform for retinopathy detection. The database consists of 35126 high-resolution retinal images taken under a variety of conditions. After training, the network shows a specificity of 93.65% and an accuracy of 83.68% on validation process.

KW - Convolutional neural network

KW - Deep learning

KW - Diabetic retinopathy

KW - Medical image classification

UR - https://www.scopus.com/pages/publications/85034233631

U2 - 10.1007/978-3-319-68612-7_72

DO - 10.1007/978-3-319-68612-7_72

M3 - Contribución a la conferencia

AN - SCOPUS:85034233631

SN - 9783319686110

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 635

EP - 642

BT - Artificial Neural Networks and Machine Learning – ICANN 2017 - 26th International Conference on Artificial Neural Networks, Proceedings

A2 - Lintas, Alessandra

A2 - Villa, Alessandro E.

A2 - Rovetta, Stefano

A2 - Verschure, Paul F.

PB - Springer Verlag

T2 - 26th International Conference on Artificial Neural Networks, ICANN 2017

Y2 - 11 September 2017 through 14 September 2017

ER -

García G, Gallardo J, Mauricio A, López J, Del Carpio C. Detection of diabetic retinopathy based on a convolutional neural network using retinal fundus images. In Lintas A, Villa AE, Rovetta S, Verschure PF, editors, Artificial Neural Networks and Machine Learning – ICANN 2017 - 26th International Conference on Artificial Neural Networks, Proceedings. Springer Verlag. 2017. p. 635-642. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-68612-7_72

Detection of diabetic retinopathy based on a convolutional neural network using retinal fundus images

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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