Diabetic Retinopathy Diagnosis Based on RA-EfficientNet

The early detection and grade diagnosis of diabetic retinopathy (DR) are very important for the avoidance of blindness, and using deep learning methods to automatically diagnose DR has attracted great attention. However, the small amount of DR data limits its application. To automatically learn the...

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Autores principales:	San-Li Yi, Xue-Lian Yang, Tian-Wei Wang, Fu-Rong She, Xin Xiong, Jian-Feng He
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	EfficientNet transfer learning residual attention block retinal image diabetic retinopathy Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/f830bb919e6248fead07491d8c76488f
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id	oai:doaj.org-article:f830bb919e6248fead07491d8c76488f
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spelling	oai:doaj.org-article:f830bb919e6248fead07491d8c76488f2021-11-25T16:43:10ZDiabetic Retinopathy Diagnosis Based on RA-EfficientNet10.3390/app1122110352076-3417https://doaj.org/article/f830bb919e6248fead07491d8c76488f2021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/11035https://doaj.org/toc/2076-3417The early detection and grade diagnosis of diabetic retinopathy (DR) are very important for the avoidance of blindness, and using deep learning methods to automatically diagnose DR has attracted great attention. However, the small amount of DR data limits its application. To automatically learn the disease’s features and detect DR more accurately, we constructed a DR grade diagnostic model. To realize the model, the authors performed the following steps: firstly, we preprocess the DR images to solve the existing problems in an APTOS 2019 dataset, such as size difference, information redundancy and the data imbalance. Secondly, to extract more valid image features, a new network named RA-EfficientNet is proposed, in which a residual attention (RA) block is added to EfficientNet to extract more features and to solve the problem of small differences between lesions. EfficientNet has been previously trained on the ImageNet dataset, based on transfer learning technology, to overcome the small sample size problem of DR. Lastly, based on the extracted features, two classifiers are designed, one is a 2-grade classifier and the other a 5-grade classifier. The 2-grade classifier can diagnose DR, and the 5-grade classifier provides 5 grades of diagnosis for DR, as follows: 0 for No DR, 1 for mild DR, 2 for moderate, 3 for severe and 4 for proliferative DR. Experiments show that our proposed RA-EfficientNet can achieve better performance, with an accuracy value of 98.36% and a kappa score of 96.72% in a 2-grade classification and an accuracy value of 93.55% and a kappa score of 91.93% in a 5-grade classification. The results indicate that the proposed model effectively improves DR detection efficiency and resolves the existing limitation of manual feature extraction.San-Li YiXue-Lian YangTian-Wei WangFu-Rong SheXin XiongJian-Feng HeMDPI AGarticleEfficientNettransfer learningresidual attention blockretinal imagediabetic retinopathyTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 11035, p 11035 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	EfficientNet transfer learning residual attention block retinal image diabetic retinopathy Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	EfficientNet transfer learning residual attention block retinal image diabetic retinopathy Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 San-Li Yi Xue-Lian Yang Tian-Wei Wang Fu-Rong She Xin Xiong Jian-Feng He Diabetic Retinopathy Diagnosis Based on RA-EfficientNet
description	The early detection and grade diagnosis of diabetic retinopathy (DR) are very important for the avoidance of blindness, and using deep learning methods to automatically diagnose DR has attracted great attention. However, the small amount of DR data limits its application. To automatically learn the disease’s features and detect DR more accurately, we constructed a DR grade diagnostic model. To realize the model, the authors performed the following steps: firstly, we preprocess the DR images to solve the existing problems in an APTOS 2019 dataset, such as size difference, information redundancy and the data imbalance. Secondly, to extract more valid image features, a new network named RA-EfficientNet is proposed, in which a residual attention (RA) block is added to EfficientNet to extract more features and to solve the problem of small differences between lesions. EfficientNet has been previously trained on the ImageNet dataset, based on transfer learning technology, to overcome the small sample size problem of DR. Lastly, based on the extracted features, two classifiers are designed, one is a 2-grade classifier and the other a 5-grade classifier. The 2-grade classifier can diagnose DR, and the 5-grade classifier provides 5 grades of diagnosis for DR, as follows: 0 for No DR, 1 for mild DR, 2 for moderate, 3 for severe and 4 for proliferative DR. Experiments show that our proposed RA-EfficientNet can achieve better performance, with an accuracy value of 98.36% and a kappa score of 96.72% in a 2-grade classification and an accuracy value of 93.55% and a kappa score of 91.93% in a 5-grade classification. The results indicate that the proposed model effectively improves DR detection efficiency and resolves the existing limitation of manual feature extraction.
format	article
author	San-Li Yi Xue-Lian Yang Tian-Wei Wang Fu-Rong She Xin Xiong Jian-Feng He
author_facet	San-Li Yi Xue-Lian Yang Tian-Wei Wang Fu-Rong She Xin Xiong Jian-Feng He
author_sort	San-Li Yi
title	Diabetic Retinopathy Diagnosis Based on RA-EfficientNet
title_short	Diabetic Retinopathy Diagnosis Based on RA-EfficientNet
title_full	Diabetic Retinopathy Diagnosis Based on RA-EfficientNet
title_fullStr	Diabetic Retinopathy Diagnosis Based on RA-EfficientNet
title_full_unstemmed	Diabetic Retinopathy Diagnosis Based on RA-EfficientNet
title_sort	diabetic retinopathy diagnosis based on ra-efficientnet
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/f830bb919e6248fead07491d8c76488f
work_keys_str_mv	AT sanliyi diabeticretinopathydiagnosisbasedonraefficientnet AT xuelianyang diabeticretinopathydiagnosisbasedonraefficientnet AT tianweiwang diabeticretinopathydiagnosisbasedonraefficientnet AT furongshe diabeticretinopathydiagnosisbasedonraefficientnet AT xinxiong diabeticretinopathydiagnosisbasedonraefficientnet AT jianfenghe diabeticretinopathydiagnosisbasedonraefficientnet
_version_	1718413054383751168

Diabetic Retinopathy Diagnosis Based on RA-EfficientNet

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