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Transfer learning with convolutional neuralnetworks for diabetic retinopathy image classification : a review
ID Kandel, Ibrahem (Author), ID Castelli, Mauro (Author)

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Abstract
Diabetic retinopathy (DR) is a dangerous eye condition that affects diabetic patients. Without early detection, it can affect the retina and may eventually cause permanent blindness. The early diagnosis of DR is crucial for its treatment. However, the diagnosis of DR is a very difficult process that requires an experienced ophthalmologist. A breakthrough in the field of artificial intelligence called deep learning can help in giving the ophthalmologist a second opinion regarding the classification of the DR by using an autonomous classifier. To accurately train a deep learning model to classify DR, an enormous number of images is required, and this is an important limitation in the DR domain. Transfer learning is a technique that can help in overcoming the scarcity of images. The main idea that is exploited by transfer learning is that a deep learning architecture, previously trained on non-medical images, can be fine-tuned to suit the DR dataset. This paper reviews research papers that focus on DR classification by using transfer learning to present the best existing methods to address this problem. This review can help future researchers to find out existing transfer learning methods to address the DR classification task and to show their differences in terms of performance.

Language:English
Work type:Article
Typology:1.02 - Review Article
Organization:EF - School of Economics and Business
Publication version:Version of Record
Year:2020
Number of pages:Str. 1-24
Numbering:Vol. 10, iss. 6 (art. 2021)
PID:20.500.12556/RUL-124241 This link opens in a new window
UDC:004:78
ISSN on article:2076-3417
DOI:10.3390/app10062021 This link opens in a new window
COBISS.SI-ID:38420483 This link opens in a new window
Publication date in RUL:12.01.2021
Views:582
Downloads:227
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Record is a part of a journal

Title:Applied sciences
Shortened title:Appl. sci.
Publisher:MDPI
ISSN:2076-3417
COBISS.SI-ID:522979353 This link opens in a new window

Licences

License:CC BY 4.0, Creative Commons Attribution 4.0 International
Link:http://creativecommons.org/licenses/by/4.0/
Description:This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
Licensing start date:12.01.2021

Projects

Funder:ARRS - Slovenian Research Agency
Project number:P5-0410
Name:Digitalizacija kot gonilo trajnostnega razvoja posameznika, organizacij in družbe

Funder:FCT - Fundação para a Ciência e a Tecnologia, I.P.
Project number:DSAIPA/DS/0022/2018
Name:GADgET

Funder:FCT - Fundação para a Ciência e a Tecnologia, I.P.
Project number:DSAIPA/DS/0113/2019
Name:AICE

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