|3/2021 - 6|
Classification of Diabetic Retinopathy disease with Transfer Learning using Deep Convolutional Neural NetworksSOMASUNDARAM, K. , SIVAKUMAR, P. , SURESH, D.
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computer aided diagnosis, image classification, learning, neural networks, retinopathy
image(12), learning(8), diabetic(8), classification(8), retinopathy(7), deep(7), recognition(6), neural(6), convolutional(5), retinal(4)
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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
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.
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
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