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Stefan cel Mare
University of Suceava
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Print ISSN: 1582-7445
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WorldCat: 643243560
doi: 10.4316/AECE


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  3/2021 - 6

Classification of Diabetic Retinopathy disease with Transfer Learning using Deep Convolutional Neural Networks

SOMASUNDARAM, K. See more information about SOMASUNDARAM, K. on SCOPUS See more information about SOMASUNDARAM, K. on IEEExplore See more information about SOMASUNDARAM, K. on Web of Science, SIVAKUMAR, P. See more information about  SIVAKUMAR, P. on SCOPUS See more information about  SIVAKUMAR, P. on SCOPUS See more information about SIVAKUMAR, P. on Web of Science, SURESH, D. See more information about SURESH, D. on SCOPUS See more information about SURESH, D. on SCOPUS See more information about SURESH, D. on Web of Science
 
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Download PDF pdficon (1,768 KB) | Citation | Downloads: 694 | Views: 449

Author keywords
computer aided diagnosis, image classification, learning, neural networks, retinopathy

References keywords
image(12), learning(8), diabetic(8), classification(8), retinopathy(7), deep(7), recognition(6), neural(6), convolutional(5), retinal(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2021-08-31
Volume 21, Issue 3, Year 2021, On page(s): 49 - 56
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2021.03006
Web of Science Accession Number: 000691632000006
SCOPUS ID: 85114777184

Abstract
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Diabetic Retinopathy (DR) stays a main source of vision deterioration around world and it is getting exacerbated day by day. Almost no warning signs for detecting DR which will be greater challenge with us today. So, it is extremely preferred that DR has to be discovered on time. Adversely, the existing result involves an ophthalmologist to manually check and identify DR by positioning the exudates related with vascular irregularity due to diabetes from fundus image. In this work, we are able to classify images based on different severity levels through an automatic DR classification system. To extract specific features of image without any loss in spatial information, a Convolutional Neural Network (CNN) models which possesses an image with a distinct weight matrix is used. In the beginning, we estimate various CNN models to conclude the best performing CNN for DR classification with an objective to obtain much better accuracy. In the classification of DR disease with transfer learning using deep CNN models, 97.72% of accuracy is provided by the proposed CNN model for Kaggle dataset. The proposed CNN model provides a classification accuracy of 97.58% for MESSIDOR dataset. The proposed technique provides better results than other state-of-art methods.


References | Cited By  «-- Click to see who has cited this paper

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References Weight

Web of Science® Citations for all references: 68,818 TCR
SCOPUS® Citations for all references: 139,619 TCR

Web of Science® Average Citations per reference: 2,647 ACR
SCOPUS® Average Citations per reference: 5,370 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2022-08-12 08:18 in 229 seconds.




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