Diabetic Retinopathy Classification With Deep Learning via Fundus Images: A Short Survey
Diabetic retinopathy (DR) is a microvascular disease that is associated with diabetes mellitus. DR can cause irreversible vision loss and low vision. DR classification, that is, early DR diagnosis and accurate DR grading, is critical for vision protection and immediate treatment. Deep learning-based...
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IEEE
2024-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10419327/ |
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| author | Shanshan Zhu Changchun Xiong Qingshan Zhong Yudong Yao |
| author_facet | Shanshan Zhu Changchun Xiong Qingshan Zhong Yudong Yao |
| author_sort | Shanshan Zhu |
| collection | DOAJ |
| description | Diabetic retinopathy (DR) is a microvascular disease that is associated with diabetes mellitus. DR can cause irreversible vision loss and low vision. DR classification, that is, early DR diagnosis and accurate DR grading, is critical for vision protection and immediate treatment. Deep learning-based automated systems led to significant expectations for DR classification based on fundus images with several advantages. In the past several years, many outstanding studies in this area have been conducted and several review articles have been published. However, the new trends and the future directions are need to furtherly analyzed. Thus, we carefully included and read 94 related articles published from 2018 to 2023 through Web of Science, PubMed, Scopus, and IEEE Xplore. From this review, we found that transfer learning has been used as an outstanding strategy for overcoming the issue of the limited data resources to support DR analysis. CNN models of ResNet and VGGNet with layers of tens or even hundreds are the most popular frameworks used for DR classification. The APTOS 2019 and EyePACS are the most widely used datasets for DR classification. In addition, some lightweight DL architectures like SqueezeNet and MobileNet have been proposed for DR classification tasks, especially for limited data resources and computational capabilities. Although deep learning has achieved or surpassed human-level accuracy in DR classification, there is still a long way to go in real clinical workflows. Further improvements in model interpretability, trustworthiness from ophthalmologists, cost-effective and reliable DR screening systems are needed. |
| format | Article |
| id | doaj-art-d0465359842441f192e883163ba6dde6 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-d0465359842441f192e883163ba6dde62025-08-20T02:49:47ZengIEEEIEEE Access2169-35362024-01-0112205402055810.1109/ACCESS.2024.336194410419327Diabetic Retinopathy Classification With Deep Learning via Fundus Images: A Short SurveyShanshan Zhu0https://orcid.org/0000-0001-9265-8595Changchun Xiong1Qingshan Zhong2Yudong Yao3https://orcid.org/0000-0003-3868-0593Research Institute of Medical and Biological Engineering, Ningbo University, Ningbo, ChinaFaculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, ChinaSchool of Materials Science and Chemical Engineering, Ningbo University, Ningbo, ChinaResearch Institute of Medical and Biological Engineering, Ningbo University, Ningbo, ChinaDiabetic retinopathy (DR) is a microvascular disease that is associated with diabetes mellitus. DR can cause irreversible vision loss and low vision. DR classification, that is, early DR diagnosis and accurate DR grading, is critical for vision protection and immediate treatment. Deep learning-based automated systems led to significant expectations for DR classification based on fundus images with several advantages. In the past several years, many outstanding studies in this area have been conducted and several review articles have been published. However, the new trends and the future directions are need to furtherly analyzed. Thus, we carefully included and read 94 related articles published from 2018 to 2023 through Web of Science, PubMed, Scopus, and IEEE Xplore. From this review, we found that transfer learning has been used as an outstanding strategy for overcoming the issue of the limited data resources to support DR analysis. CNN models of ResNet and VGGNet with layers of tens or even hundreds are the most popular frameworks used for DR classification. The APTOS 2019 and EyePACS are the most widely used datasets for DR classification. In addition, some lightweight DL architectures like SqueezeNet and MobileNet have been proposed for DR classification tasks, especially for limited data resources and computational capabilities. Although deep learning has achieved or surpassed human-level accuracy in DR classification, there is still a long way to go in real clinical workflows. Further improvements in model interpretability, trustworthiness from ophthalmologists, cost-effective and reliable DR screening systems are needed.https://ieeexplore.ieee.org/document/10419327/Classificationdiabetic retinopathydeep learningfundus images |
| spellingShingle | Shanshan Zhu Changchun Xiong Qingshan Zhong Yudong Yao Diabetic Retinopathy Classification With Deep Learning via Fundus Images: A Short Survey IEEE Access Classification diabetic retinopathy deep learning fundus images |
| title | Diabetic Retinopathy Classification With Deep Learning via Fundus Images: A Short Survey |
| title_full | Diabetic Retinopathy Classification With Deep Learning via Fundus Images: A Short Survey |
| title_fullStr | Diabetic Retinopathy Classification With Deep Learning via Fundus Images: A Short Survey |
| title_full_unstemmed | Diabetic Retinopathy Classification With Deep Learning via Fundus Images: A Short Survey |
| title_short | Diabetic Retinopathy Classification With Deep Learning via Fundus Images: A Short Survey |
| title_sort | diabetic retinopathy classification with deep learning via fundus images a short survey |
| topic | Classification diabetic retinopathy deep learning fundus images |
| url | https://ieeexplore.ieee.org/document/10419327/ |
| work_keys_str_mv | AT shanshanzhu diabeticretinopathyclassificationwithdeeplearningviafundusimagesashortsurvey AT changchunxiong diabeticretinopathyclassificationwithdeeplearningviafundusimagesashortsurvey AT qingshanzhong diabeticretinopathyclassificationwithdeeplearningviafundusimagesashortsurvey AT yudongyao diabeticretinopathyclassificationwithdeeplearningviafundusimagesashortsurvey |