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

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

90 Citas (Scopus)

Resumen

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.

Idioma original	Inglés
Título de la publicación alojada	Artificial Neural Networks and Machine Learning – ICANN 2017 - 26th International Conference on Artificial Neural Networks, Proceedings
Editores	Alessandra Lintas, Alessandro E. Villa, Stefano Rovetta, Paul F. Verschure
Editorial	Springer Verlag
Páginas	635-642
Número de páginas	8
ISBN (versión impresa)	9783319686110
DOI	https://doi.org/10.1007/978-3-319-68612-7_72
Estado	Publicada - 2017
Publicado de forma externa	Sí
Evento	26th International Conference on Artificial Neural Networks, ICANN 2017 - Alghero, Italia Duración: 11 set. 2017 → 14 set. 2017

Serie de la publicación

Nombre	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen	10614 LNCS
ISSN (versión impresa)	0302-9743
ISSN (versión digital)	1611-3349

Conferencia

Conferencia	26th International Conference on Artificial Neural Networks, ICANN 2017
País/Territorio	Italia
Ciudad	Alghero
Período	11/09/17 → 14/09/17

ODS de las Naciones Unidas

Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

ODS 3: Salud y bienestar

Acceder al documento

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

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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. En 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)).

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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",

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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. En 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, Italia, 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).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

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. En Lintas A, Villa AE, Rovetta S, Verschure PF, editores, 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

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