Classification of diabetic retinopathy stages using image segmentation and an artificial neural network

Narit Hnoohom; Ratikanlaya Tanthuwapathom

doi:10.1007/978-3-319-60675-0_5

Classification of diabetic retinopathy stages using image segmentation and an artificial neural network

Narit Hnoohom
, Ratikanlaya Tanthuwapathom

Department of Computer Engineering

Mahidol University

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

2 Citations (Scopus)

Abstract

Diabetic retinopathy, which can lead to blindness, has been found in 22% of diabetic patients in the latest survey. Therefore, diabetic patients should have an eye examination at least once a year. However, it has been found that currently there is a problematic lack of specialists in ophthalmology. Detection and treatment of diabetic retinopathy are thus delayed. The idea to create a classification system of diabetic retinopathy stages to facilitate the making of preliminary decisions by ophthalmologists is introduced. This paper presents the classification of diabetic retinopathy stages using image segmentation and an artificial neural network. This proposed method applies local thresholding to separate the foreground region from the background region so that the optic disc and exudates regions are able to be identified more clearly. The experiment was carried out with 100 fundus images from the Institute of Medical Research and Technology Assessment database. The prediction model had an accuracy rate of up to 96%.

Original language	English
Title of host publication	Trends in Artificial Intelligence
Subtitle of host publication	PRICAI 2016 Workshops - PeHealth 2016, I3A 2016, AIED 2016, AI4T 2016, IWEC 2016, and RSAI 2016, Revised Selected Papers
Editors	Thanaruk Theeramunkong, Patiyuth Pramkeaw, Mahasak Ketcham, Masayuki Numao, Thepchai Supnithi, Narit Hnoohom
Publisher	Springer Verlag
Pages	51-62
Number of pages	12
ISBN (Print)	9783319606743
DOIs	https://doi.org/10.1007/978-3-319-60675-0_5
Publication status	Published - 2017
Event	14th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2016 - Phuket, Thailand Duration: 22 Aug 2016 → 26 Aug 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10004 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	14th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2016
Country/Territory	Thailand
City	Phuket
Period	22/08/16 → 26/08/16

Keywords

Artificial neural network
Diabetic retinopathy
Exudates
Fundus image

Access to Document

10.1007/978-3-319-60675-0_5

Cite this

Hnoohom, N., & Tanthuwapathom, R. (2017). Classification of diabetic retinopathy stages using image segmentation and an artificial neural network. In T. Theeramunkong, P. Pramkeaw, M. Ketcham, M. Numao, T. Supnithi, & N. Hnoohom (Eds.), Trends in Artificial Intelligence: PRICAI 2016 Workshops - PeHealth 2016, I3A 2016, AIED 2016, AI4T 2016, IWEC 2016, and RSAI 2016, Revised Selected Papers (pp. 51-62). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10004 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-60675-0_5

Hnoohom, Narit ; Tanthuwapathom, Ratikanlaya. / Classification of diabetic retinopathy stages using image segmentation and an artificial neural network. Trends in Artificial Intelligence: PRICAI 2016 Workshops - PeHealth 2016, I3A 2016, AIED 2016, AI4T 2016, IWEC 2016, and RSAI 2016, Revised Selected Papers. editor / Thanaruk Theeramunkong ; Patiyuth Pramkeaw ; Mahasak Ketcham ; Masayuki Numao ; Thepchai Supnithi ; Narit Hnoohom. Springer Verlag, 2017. pp. 51-62 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Classification of diabetic retinopathy stages using image segmentation and an artificial neural network",

abstract = "Diabetic retinopathy, which can lead to blindness, has been found in 22\% of diabetic patients in the latest survey. Therefore, diabetic patients should have an eye examination at least once a year. However, it has been found that currently there is a problematic lack of specialists in ophthalmology. Detection and treatment of diabetic retinopathy are thus delayed. The idea to create a classification system of diabetic retinopathy stages to facilitate the making of preliminary decisions by ophthalmologists is introduced. This paper presents the classification of diabetic retinopathy stages using image segmentation and an artificial neural network. This proposed method applies local thresholding to separate the foreground region from the background region so that the optic disc and exudates regions are able to be identified more clearly. The experiment was carried out with 100 fundus images from the Institute of Medical Research and Technology Assessment database. The prediction model had an accuracy rate of up to 96\%.",

keywords = "Artificial neural network, Diabetic retinopathy, Exudates, Fundus image",

author = "Narit Hnoohom and Ratikanlaya Tanthuwapathom",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 14th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2016 ; Conference date: 22-08-2016 Through 26-08-2016",

year = "2017",

doi = "10.1007/978-3-319-60675-0\_5",

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

publisher = "Springer Verlag",

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editor = "Thanaruk Theeramunkong and Patiyuth Pramkeaw and Mahasak Ketcham and Masayuki Numao and Thepchai Supnithi and Narit Hnoohom",

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Hnoohom, N & Tanthuwapathom, R 2017, Classification of diabetic retinopathy stages using image segmentation and an artificial neural network. in T Theeramunkong, P Pramkeaw, M Ketcham, M Numao, T Supnithi & N Hnoohom (eds), Trends in Artificial Intelligence: PRICAI 2016 Workshops - PeHealth 2016, I3A 2016, AIED 2016, AI4T 2016, IWEC 2016, and RSAI 2016, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10004 LNAI, Springer Verlag, pp. 51-62, 14th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2016, Phuket, Thailand, 22/08/16. https://doi.org/10.1007/978-3-319-60675-0_5

Classification of diabetic retinopathy stages using image segmentation and an artificial neural network. / Hnoohom, Narit; Tanthuwapathom, Ratikanlaya.
Trends in Artificial Intelligence: PRICAI 2016 Workshops - PeHealth 2016, I3A 2016, AIED 2016, AI4T 2016, IWEC 2016, and RSAI 2016, Revised Selected Papers. ed. / Thanaruk Theeramunkong; Patiyuth Pramkeaw; Mahasak Ketcham; Masayuki Numao; Thepchai Supnithi; Narit Hnoohom. Springer Verlag, 2017. p. 51-62 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10004 LNAI).

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

TY - GEN

T1 - Classification of diabetic retinopathy stages using image segmentation and an artificial neural network

AU - Hnoohom, Narit

AU - Tanthuwapathom, Ratikanlaya

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

PY - 2017

Y1 - 2017

N2 - Diabetic retinopathy, which can lead to blindness, has been found in 22% of diabetic patients in the latest survey. Therefore, diabetic patients should have an eye examination at least once a year. However, it has been found that currently there is a problematic lack of specialists in ophthalmology. Detection and treatment of diabetic retinopathy are thus delayed. The idea to create a classification system of diabetic retinopathy stages to facilitate the making of preliminary decisions by ophthalmologists is introduced. This paper presents the classification of diabetic retinopathy stages using image segmentation and an artificial neural network. This proposed method applies local thresholding to separate the foreground region from the background region so that the optic disc and exudates regions are able to be identified more clearly. The experiment was carried out with 100 fundus images from the Institute of Medical Research and Technology Assessment database. The prediction model had an accuracy rate of up to 96%.

AB - Diabetic retinopathy, which can lead to blindness, has been found in 22% of diabetic patients in the latest survey. Therefore, diabetic patients should have an eye examination at least once a year. However, it has been found that currently there is a problematic lack of specialists in ophthalmology. Detection and treatment of diabetic retinopathy are thus delayed. The idea to create a classification system of diabetic retinopathy stages to facilitate the making of preliminary decisions by ophthalmologists is introduced. This paper presents the classification of diabetic retinopathy stages using image segmentation and an artificial neural network. This proposed method applies local thresholding to separate the foreground region from the background region so that the optic disc and exudates regions are able to be identified more clearly. The experiment was carried out with 100 fundus images from the Institute of Medical Research and Technology Assessment database. The prediction model had an accuracy rate of up to 96%.

KW - Artificial neural network

KW - Diabetic retinopathy

KW - Exudates

KW - Fundus image

UR - https://www.scopus.com/pages/publications/85022333540

U2 - 10.1007/978-3-319-60675-0_5

DO - 10.1007/978-3-319-60675-0_5

M3 - Conference contribution

AN - SCOPUS:85022333540

SN - 9783319606743

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

SP - 51

EP - 62

BT - Trends in Artificial Intelligence

A2 - Theeramunkong, Thanaruk

A2 - Pramkeaw, Patiyuth

A2 - Ketcham, Mahasak

A2 - Numao, Masayuki

A2 - Supnithi, Thepchai

A2 - Hnoohom, Narit

PB - Springer Verlag

T2 - 14th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2016

Y2 - 22 August 2016 through 26 August 2016

ER -

Hnoohom N, Tanthuwapathom R. Classification of diabetic retinopathy stages using image segmentation and an artificial neural network. In Theeramunkong T, Pramkeaw P, Ketcham M, Numao M, Supnithi T, Hnoohom N, editors, Trends in Artificial Intelligence: PRICAI 2016 Workshops - PeHealth 2016, I3A 2016, AIED 2016, AI4T 2016, IWEC 2016, and RSAI 2016, Revised Selected Papers. Springer Verlag. 2017. p. 51-62. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-60675-0_5

Classification of diabetic retinopathy stages using image segmentation and an artificial neural network

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