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Cited by 484

The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.

Deep convolutional neural networks for diabetic retinopathy detection by image classification

Wan, Shaohua, Liang, Yan, Zhang, Yin·2018·Computers & Electrical Engineering

Abstract

Abstract Diabetic retinopathy (DR) is a common complication of diabetes and one of the major causes of blindness in the active population. Many of the complications of DR can be prevented by blood glucose control and timely treatment. Since the varieties and the complexities of DR, it is really difficult for DR detection in the time-consuming manual diagnosis. This paper is to attempt towards finding an automatic way to classify a given set of fundus images. We bring convolutional neural networks (CNNs) power to DR detection, which includes 3 major difficult challenges: classification, segmentation and detection. Coupled with transfer learning and hyper-parameter tuning, we adopt AlexNet, VggNet, GoogleNet, ResNet, and analyze how well these models do with the DR image classification. We employ publicly available Kaggle platform for training these models. The best classification accuracy is 95.68% and the results have demonstrated the better accuracy of CNNs and transfer learning on DR image classification.

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Crossref · 10.1016/j.compeleceng.2018.07.042

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Metadata

Authors
Wan, S., Liang, Y., & Zhang, Y.
Venue
Computers & Electrical Engineering
Publisher
Elsevier BV
Published
2018-11
Volume
72
Pages
274-282
ISSN
0045-7906 (ISSN-L)
OpenAlex
W2894558420
Cited by
484
References
28
FWCI
24.73
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Deep convolutional neural networks for diabetic retinopathy detection by image classification. https://app.cochat.ai/papers/10.1016/j.compeleceng.2018.07.042
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Wan, S., Liang, Y., & Zhang, Y. (2018). Deep convolutional neural networks for diabetic retinopathy detection by image classification. Computers & Electrical Engineering. https://doi.org/10.1016/j.compeleceng.2018.07.042

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.