Data Balancing Techniques Evaluation on Convolutional Neural Network to Classify the Diabetic Retinopathy of Fundus Image

Vina Alvionita; Mohammad Nuh; Nada Fitrieyatul Hikmah

doi:10.1109/CENIM51130.2020.9297940

Data Balancing Techniques Evaluation on Convolutional Neural Network to Classify the Diabetic Retinopathy of Fundus Image

Institut Teknologi Sepuluh Nopember

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

1 Citation (Scopus)

Abstract

Diabetic retinopathy (DR) is a common complication diabetic patients that causes impaired vision, and may even lead to blindness. Several studies on the DR diagnosis based on Computer-Aided Diagnosis (CAD) had been conducted. The method used various feature extraction modules and a particular classifier. However, this method required a long step. In a different circumstance, deep neural networks had been successfully applied in various fields and showing good performance. For this reason, we proposed a classification system for DR based on Convolutional Neural Networks (CNN). In this study, we used retina images dataset from the Asia-Pacific Tele-Ophthalmology Society (APTOS) to train CNN under three different conditions. Sequentially is imbalanced, balanced by undersampling, and balanced by oversampling. The best results were obtained in the third condition, with an accuracy of 73.64%, precision 59.01%, sensitivity 60.69%, and specificity 93.49%. The classification method in the proposed study should be realized in clinical use.

Original language	English
Title of host publication	CENIM 2020 - Proceeding
Subtitle of host publication	International Conference on Computer Engineering, Network, and Intelligent Multimedia 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	354-359
Number of pages	6
ISBN (Electronic)	9781728182834
DOIs	https://doi.org/10.1109/CENIM51130.2020.9297940
Publication status	Published - 17 Nov 2020
Event	2020 International Conference on Computer Engineering, Network, and Intelligent Multimedia, CENIM 2020 - Virtual, Surabaya, Indonesia Duration: 17 Nov 2020 → 18 Nov 2020

Publication series

Name	CENIM 2020 - Proceeding: International Conference on Computer Engineering, Network, and Intelligent Multimedia 2020

Conference

Conference	2020 International Conference on Computer Engineering, Network, and Intelligent Multimedia, CENIM 2020
Country/Territory	Indonesia
City	Virtual, Surabaya
Period	17/11/20 → 18/11/20

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

convolutional neural network
diabetic retinopathy
image classification

Access to Document

10.1109/CENIM51130.2020.9297940

Cite this

Alvionita, V., Nuh, M., & Hikmah, N. F. (2020). Data Balancing Techniques Evaluation on Convolutional Neural Network to Classify the Diabetic Retinopathy of Fundus Image. In CENIM 2020 - Proceeding: International Conference on Computer Engineering, Network, and Intelligent Multimedia 2020 (pp. 354-359). Article 9297940 (CENIM 2020 - Proceeding: International Conference on Computer Engineering, Network, and Intelligent Multimedia 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CENIM51130.2020.9297940

Alvionita, Vina ; Nuh, Mohammad ; Hikmah, Nada Fitrieyatul. / Data Balancing Techniques Evaluation on Convolutional Neural Network to Classify the Diabetic Retinopathy of Fundus Image. CENIM 2020 - Proceeding: International Conference on Computer Engineering, Network, and Intelligent Multimedia 2020. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 354-359 (CENIM 2020 - Proceeding: International Conference on Computer Engineering, Network, and Intelligent Multimedia 2020).

@inproceedings{7a6484666a8544bfbf8a8556aa7a9d9f,

title = "Data Balancing Techniques Evaluation on Convolutional Neural Network to Classify the Diabetic Retinopathy of Fundus Image",

abstract = "Diabetic retinopathy (DR) is a common complication diabetic patients that causes impaired vision, and may even lead to blindness. Several studies on the DR diagnosis based on Computer-Aided Diagnosis (CAD) had been conducted. The method used various feature extraction modules and a particular classifier. However, this method required a long step. In a different circumstance, deep neural networks had been successfully applied in various fields and showing good performance. For this reason, we proposed a classification system for DR based on Convolutional Neural Networks (CNN). In this study, we used retina images dataset from the Asia-Pacific Tele-Ophthalmology Society (APTOS) to train CNN under three different conditions. Sequentially is imbalanced, balanced by undersampling, and balanced by oversampling. The best results were obtained in the third condition, with an accuracy of 73.64\%, precision 59.01\%, sensitivity 60.69\%, and specificity 93.49\%. The classification method in the proposed study should be realized in clinical use.",

keywords = "convolutional neural network, diabetic retinopathy, image classification",

author = "Vina Alvionita and Mohammad Nuh and Hikmah, \{Nada Fitrieyatul\}",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 International Conference on Computer Engineering, Network, and Intelligent Multimedia, CENIM 2020 ; Conference date: 17-11-2020 Through 18-11-2020",

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language = "English",

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Alvionita, V, Nuh, M & Hikmah, NF 2020, Data Balancing Techniques Evaluation on Convolutional Neural Network to Classify the Diabetic Retinopathy of Fundus Image. in CENIM 2020 - Proceeding: International Conference on Computer Engineering, Network, and Intelligent Multimedia 2020., 9297940, CENIM 2020 - Proceeding: International Conference on Computer Engineering, Network, and Intelligent Multimedia 2020, Institute of Electrical and Electronics Engineers Inc., pp. 354-359, 2020 International Conference on Computer Engineering, Network, and Intelligent Multimedia, CENIM 2020, Virtual, Surabaya, Indonesia, 17/11/20. https://doi.org/10.1109/CENIM51130.2020.9297940

Data Balancing Techniques Evaluation on Convolutional Neural Network to Classify the Diabetic Retinopathy of Fundus Image. / Alvionita, Vina; Nuh, Mohammad ; Hikmah, Nada Fitrieyatul.
CENIM 2020 - Proceeding: International Conference on Computer Engineering, Network, and Intelligent Multimedia 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 354-359 9297940 (CENIM 2020 - Proceeding: International Conference on Computer Engineering, Network, and Intelligent Multimedia 2020).

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

TY - GEN

T1 - Data Balancing Techniques Evaluation on Convolutional Neural Network to Classify the Diabetic Retinopathy of Fundus Image

AU - Alvionita, Vina

AU - Nuh, Mohammad

AU - Hikmah, Nada Fitrieyatul

PY - 2020/11/17

Y1 - 2020/11/17

N2 - Diabetic retinopathy (DR) is a common complication diabetic patients that causes impaired vision, and may even lead to blindness. Several studies on the DR diagnosis based on Computer-Aided Diagnosis (CAD) had been conducted. The method used various feature extraction modules and a particular classifier. However, this method required a long step. In a different circumstance, deep neural networks had been successfully applied in various fields and showing good performance. For this reason, we proposed a classification system for DR based on Convolutional Neural Networks (CNN). In this study, we used retina images dataset from the Asia-Pacific Tele-Ophthalmology Society (APTOS) to train CNN under three different conditions. Sequentially is imbalanced, balanced by undersampling, and balanced by oversampling. The best results were obtained in the third condition, with an accuracy of 73.64%, precision 59.01%, sensitivity 60.69%, and specificity 93.49%. The classification method in the proposed study should be realized in clinical use.

AB - Diabetic retinopathy (DR) is a common complication diabetic patients that causes impaired vision, and may even lead to blindness. Several studies on the DR diagnosis based on Computer-Aided Diagnosis (CAD) had been conducted. The method used various feature extraction modules and a particular classifier. However, this method required a long step. In a different circumstance, deep neural networks had been successfully applied in various fields and showing good performance. For this reason, we proposed a classification system for DR based on Convolutional Neural Networks (CNN). In this study, we used retina images dataset from the Asia-Pacific Tele-Ophthalmology Society (APTOS) to train CNN under three different conditions. Sequentially is imbalanced, balanced by undersampling, and balanced by oversampling. The best results were obtained in the third condition, with an accuracy of 73.64%, precision 59.01%, sensitivity 60.69%, and specificity 93.49%. The classification method in the proposed study should be realized in clinical use.

KW - convolutional neural network

KW - diabetic retinopathy

KW - image classification

UR - https://www.scopus.com/pages/publications/85099651028

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DO - 10.1109/CENIM51130.2020.9297940

M3 - Conference contribution

AN - SCOPUS:85099651028

T3 - CENIM 2020 - Proceeding: International Conference on Computer Engineering, Network, and Intelligent Multimedia 2020

SP - 354

EP - 359

BT - CENIM 2020 - Proceeding

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2020 International Conference on Computer Engineering, Network, and Intelligent Multimedia, CENIM 2020

Y2 - 17 November 2020 through 18 November 2020

ER -

Alvionita V, Nuh M , Hikmah NF. Data Balancing Techniques Evaluation on Convolutional Neural Network to Classify the Diabetic Retinopathy of Fundus Image. In CENIM 2020 - Proceeding: International Conference on Computer Engineering, Network, and Intelligent Multimedia 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 354-359. 9297940. (CENIM 2020 - Proceeding: International Conference on Computer Engineering, Network, and Intelligent Multimedia 2020). doi: 10.1109/CENIM51130.2020.9297940

Data Balancing Techniques Evaluation on Convolutional Neural Network to Classify the Diabetic Retinopathy of Fundus Image

Abstract

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UN SDGs

Keywords

Access to Document

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