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Deep Learning-Based Diabetic Retinopathy Detection: A Survey

Deep Learning-Based Diabetic Retinopathy Detection: A Survey

Mohamed Jebran P., Sufia Banu
Copyright: © 2021 |Volume: 11 |Issue: 3 |Pages: 11
ISSN: 1947-9344|EISSN: 1947-9352|EISBN13: 9781799861188|DOI: 10.4018/IJOCI.2021070103
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MLA

Mohamed Jebran P., and Sufia Banu. "Deep Learning-Based Diabetic Retinopathy Detection: A Survey." IJOCI vol.11, no.3 2021: pp.38-48. http://doi.org/10.4018/IJOCI.2021070103

APA

Mohamed Jebran P. & Banu, S. (2021). Deep Learning-Based Diabetic Retinopathy Detection: A Survey. International Journal of Organizational and Collective Intelligence (IJOCI), 11(3), 38-48. http://doi.org/10.4018/IJOCI.2021070103

Chicago

Mohamed Jebran P., and Sufia Banu. "Deep Learning-Based Diabetic Retinopathy Detection: A Survey," International Journal of Organizational and Collective Intelligence (IJOCI) 11, no.3: 38-48. http://doi.org/10.4018/IJOCI.2021070103

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Abstract

Artificial intelligence (AI) is rapidly evolving from machine learning (ML) to deep learning (DL), which has ignited particular interest in ophthalmology as well. Deep learning has been applied in ophthalmology to fundus photographs, which achieve robust classification performance in the detection of diabetic retinopathy (DR). Diabetic retinopathy is a progressive condition observed in people who have had multiple years of diabetes mellitus. This paper focuses on examining how a deep learning algorithm can be applied for the detection and classification of diabetic retinopathy, both at the image level and at the lesion level. The performance of various neural networks is summarized by taking into account the sensitivity, precision, accuracy with respect to the size of the test datasets. Deep learning problems are discussed at the end.

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