General deep learning model for detecting diabetic retinopathy

Abstract Background Doctors can detect symptoms of diabetic retinopathy (DR) early by using retinal ophthalmoscopy, and they can improve diagnostic efficiency with the assistance of deep learning to select treatments and support personnel workflow. Conventionally, most deep learning methods for DR d...

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Autores principales:	Ping-Nan Chen, Chia-Chiang Lee, Chang-Min Liang, Shu-I Pao, Ke-Hao Huang, Ke-Feng Lin
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	SMOTE Overfitting Decision tree Nasnet-large Transfer learning Computer applications to medicine. Medical informatics R858-859.7 Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/abd51fbc76304968aaa59243518d4b12
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spelling	oai:doaj.org-article:abd51fbc76304968aaa59243518d4b122021-11-14T12:12:55ZGeneral deep learning model for detecting diabetic retinopathy10.1186/s12859-021-04005-x1471-2105https://doaj.org/article/abd51fbc76304968aaa59243518d4b122021-11-01T00:00:00Zhttps://doi.org/10.1186/s12859-021-04005-xhttps://doaj.org/toc/1471-2105Abstract Background Doctors can detect symptoms of diabetic retinopathy (DR) early by using retinal ophthalmoscopy, and they can improve diagnostic efficiency with the assistance of deep learning to select treatments and support personnel workflow. Conventionally, most deep learning methods for DR diagnosis categorize retinal ophthalmoscopy images into training and validation data sets according to the 80/20 rule, and they use the synthetic minority oversampling technique (SMOTE) in data processing (e.g., rotating, scaling, and translating training images) to increase the number of training samples. Oversampling training may lead to overfitting of the training model. Therefore, untrained or unverified images can yield erroneous predictions. Although the accuracy of prediction results is 90%–99%, this overfitting of training data may distort training module variables. Results This study uses a 2-stage training method to solve the overfitting problem. In the training phase, to build the model, the Learning module 1 used to identify the DR and no-DR. The Learning module 2 on SMOTE synthetic datasets to identify the mild-NPDR, moderate NPDR, severe NPDR and proliferative DR classification. These two modules also used early stopping and data dividing methods to reduce overfitting by oversampling. In the test phase, we use the DIARETDB0, DIARETDB1, eOphtha, MESSIDOR, and DRIVE datasets to evaluate the performance of the training network. The prediction accuracy achieved to 85.38%, 84.27%, 85.75%, 86.73%, and 92.5%. Conclusions Based on the experiment, a general deep learning model for detecting DR was developed, and it could be used with all DR databases. We provided a simple method of addressing the imbalance of DR databases, and this method can be used with other medical images.Ping-Nan ChenChia-Chiang LeeChang-Min LiangShu-I PaoKe-Hao HuangKe-Feng LinBMCarticleSMOTEOverfittingDecision treeNasnet-largeTransfer learningComputer applications to medicine. Medical informaticsR858-859.7Biology (General)QH301-705.5ENBMC Bioinformatics, Vol 22, Iss S5, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	SMOTE Overfitting Decision tree Nasnet-large Transfer learning Computer applications to medicine. Medical informatics R858-859.7 Biology (General) QH301-705.5
spellingShingle	SMOTE Overfitting Decision tree Nasnet-large Transfer learning Computer applications to medicine. Medical informatics R858-859.7 Biology (General) QH301-705.5 Ping-Nan Chen Chia-Chiang Lee Chang-Min Liang Shu-I Pao Ke-Hao Huang Ke-Feng Lin General deep learning model for detecting diabetic retinopathy
description	Abstract Background Doctors can detect symptoms of diabetic retinopathy (DR) early by using retinal ophthalmoscopy, and they can improve diagnostic efficiency with the assistance of deep learning to select treatments and support personnel workflow. Conventionally, most deep learning methods for DR diagnosis categorize retinal ophthalmoscopy images into training and validation data sets according to the 80/20 rule, and they use the synthetic minority oversampling technique (SMOTE) in data processing (e.g., rotating, scaling, and translating training images) to increase the number of training samples. Oversampling training may lead to overfitting of the training model. Therefore, untrained or unverified images can yield erroneous predictions. Although the accuracy of prediction results is 90%–99%, this overfitting of training data may distort training module variables. Results This study uses a 2-stage training method to solve the overfitting problem. In the training phase, to build the model, the Learning module 1 used to identify the DR and no-DR. The Learning module 2 on SMOTE synthetic datasets to identify the mild-NPDR, moderate NPDR, severe NPDR and proliferative DR classification. These two modules also used early stopping and data dividing methods to reduce overfitting by oversampling. In the test phase, we use the DIARETDB0, DIARETDB1, eOphtha, MESSIDOR, and DRIVE datasets to evaluate the performance of the training network. The prediction accuracy achieved to 85.38%, 84.27%, 85.75%, 86.73%, and 92.5%. Conclusions Based on the experiment, a general deep learning model for detecting DR was developed, and it could be used with all DR databases. We provided a simple method of addressing the imbalance of DR databases, and this method can be used with other medical images.
format	article
author	Ping-Nan Chen Chia-Chiang Lee Chang-Min Liang Shu-I Pao Ke-Hao Huang Ke-Feng Lin
author_facet	Ping-Nan Chen Chia-Chiang Lee Chang-Min Liang Shu-I Pao Ke-Hao Huang Ke-Feng Lin
author_sort	Ping-Nan Chen
title	General deep learning model for detecting diabetic retinopathy
title_short	General deep learning model for detecting diabetic retinopathy
title_full	General deep learning model for detecting diabetic retinopathy
title_fullStr	General deep learning model for detecting diabetic retinopathy
title_full_unstemmed	General deep learning model for detecting diabetic retinopathy
title_sort	general deep learning model for detecting diabetic retinopathy
publisher	BMC
publishDate	2021
url	https://doaj.org/article/abd51fbc76304968aaa59243518d4b12
work_keys_str_mv	AT pingnanchen generaldeeplearningmodelfordetectingdiabeticretinopathy AT chiachianglee generaldeeplearningmodelfordetectingdiabeticretinopathy AT changminliang generaldeeplearningmodelfordetectingdiabeticretinopathy AT shuipao generaldeeplearningmodelfordetectingdiabeticretinopathy AT kehaohuang generaldeeplearningmodelfordetectingdiabeticretinopathy AT kefenglin generaldeeplearningmodelfordetectingdiabeticretinopathy
_version_	1718429366573072384

General deep learning model for detecting diabetic retinopathy

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