Coarse-to-fine classification for diabetic retinopathy grading using convolutional neural network

doi:10.1016/j.artmed.2020.101936

. 2020 Aug:108:101936.

doi: 10.1016/j.artmed.2020.101936. Epub 2020 Jul 24.

Coarse-to-fine classification for diabetic retinopathy grading using convolutional neural network

Zhan Wu¹, Gonglei Shi², Yang Chen³, Fei Shi⁴, Xinjian Chen⁵, Gouenou Coatrieux⁶, Jian Yang⁷, Limin Luo⁸, Shuo Li⁹

Affiliations

¹ School of Cyberspace Security, Southeast University, Nanjing, Jiangsu, China.
² Laboratory of Image Science and Technology, School of Computer Science and Engineering, Southeast University, Nanjing, China.
³ School of Cyberspace Security, Southeast University, Nanjing, Jiangsu, China; Laboratory of Image Science and Technology, School of Computer Science and Engineering, Southeast University, Nanjing, China; Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, Nanjing, China; Centre de Recherche en Information Biomedicale Sino-Francais (LIA CRIBs), Rennes, France. Electronic address: chenyang.list@seu.edu.cn.
⁴ School of Electronics and Information Engineering, Soochow University, Suzhou, China.
⁵ School of Electronics and Information Engineering, Soochow University, Suzhou, China. Electronic address: xjchen@suda.edu.cn.
⁶ Mines-Telecom, Telecom Bretagne, INSERM U1101 LaTIM, Brest, France.
⁷ School of Optoelectronics, Beijing Institute of Technology, Beijing, China.
⁸ School of Cyberspace Security, Southeast University, Nanjing, Jiangsu, China; Laboratory of Image Science and Technology, School of Computer Science and Engineering, Southeast University, Nanjing, China; Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, Nanjing, China; Centre de Recherche en Information Biomedicale Sino-Francais (LIA CRIBs), Rennes, France.
⁹ Department of Medical Imaging, Western University, London, Canada.

PMID: 32972665
DOI: 10.1016/j.artmed.2020.101936

Free article

Coarse-to-fine classification for diabetic retinopathy grading using convolutional neural network

Zhan Wu et al. Artif Intell Med. 2020 Aug.

Free article

. 2020 Aug:108:101936.

doi: 10.1016/j.artmed.2020.101936. Epub 2020 Jul 24.

Authors

Zhan Wu¹, Gonglei Shi², Yang Chen³, Fei Shi⁴, Xinjian Chen⁵, Gouenou Coatrieux⁶, Jian Yang⁷, Limin Luo⁸, Shuo Li⁹

Affiliations

¹ School of Cyberspace Security, Southeast University, Nanjing, Jiangsu, China.
² Laboratory of Image Science and Technology, School of Computer Science and Engineering, Southeast University, Nanjing, China.
³ School of Cyberspace Security, Southeast University, Nanjing, Jiangsu, China; Laboratory of Image Science and Technology, School of Computer Science and Engineering, Southeast University, Nanjing, China; Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, Nanjing, China; Centre de Recherche en Information Biomedicale Sino-Francais (LIA CRIBs), Rennes, France. Electronic address: chenyang.list@seu.edu.cn.
⁴ School of Electronics and Information Engineering, Soochow University, Suzhou, China.
⁵ School of Electronics and Information Engineering, Soochow University, Suzhou, China. Electronic address: xjchen@suda.edu.cn.
⁶ Mines-Telecom, Telecom Bretagne, INSERM U1101 LaTIM, Brest, France.
⁷ School of Optoelectronics, Beijing Institute of Technology, Beijing, China.
⁸ School of Cyberspace Security, Southeast University, Nanjing, Jiangsu, China; Laboratory of Image Science and Technology, School of Computer Science and Engineering, Southeast University, Nanjing, China; Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, Nanjing, China; Centre de Recherche en Information Biomedicale Sino-Francais (LIA CRIBs), Rennes, France.
⁹ Department of Medical Imaging, Western University, London, Canada.

PMID: 32972665
DOI: 10.1016/j.artmed.2020.101936

Abstract

Diabetic retinopathy (DR) is the most common eye complication of diabetes and one of the leading causes of blindness and vision impairment. Automated and accurate DR grading is of great significance for the timely and effective treatment of fundus diseases. Current clinical methods remain subject to potential time-consumption and high-risk. In this paper, a hierarchically Coarse-to-fine network (CF-DRNet) is proposed as an automatic clinical tool to classify five stages of DR severity grades using convolutional neural networks (CNNs). The CF-DRNet conforms to the hierarchical characteristic of DR grading and effectively improves the classification performance of five-class DR grading, which consists of the following: (1) The Coarse Network performs two-class classification including No DR and DR, where the attention gate module highlights the salient lesion features and suppresses irrelevant background information. (2) The Fine Network is proposed to classify four stages of DR severity grades of the grade DR from the Coarse Network including mild, moderate, severe non-proliferative DR (NPDR) and proliferative DR (PDR). Experimental results show that proposed CF-DRNet outperforms some state-of-art methods in the publicly available IDRiD and Kaggle fundus image datasets. These results indicate our method enables an efficient and reliable DR grading diagnosis in clinic.

Keywords: Coarse-to-fine classification; Convolutional neural networks; Diabetic retinopathy grading; Fundus images.

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Coarse-to-fine classification for diabetic retinopathy grading using convolutional neural network

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Coarse-to-fine classification for diabetic retinopathy grading using convolutional neural network

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