Comparative Study of Transfer Learning Models for Retinal Disease Diagnosis from Fundus Images
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Pin, Kuntha | - |
dc.contributor.author | Chang, Jee Ho | - |
dc.contributor.author | Nam, Yunyoung | - |
dc.date.accessioned | 2022-02-03T02:40:32Z | - |
dc.date.available | 2022-02-03T02:40:32Z | - |
dc.date.issued | 2022-01 | - |
dc.identifier.issn | 1546-2218 | - |
dc.identifier.issn | 1546-2226 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/sch/handle/2021.sw.sch/20314 | - |
dc.description.abstract | While the usage of digital ocular fundus image has been widespread in ophthalmology practice, the interpretation of the image has been still on the hands of the ophthalmologists which are quite costly. We explored a robust deep learning system that detects three major ocular diseases: diabetic retinopathy (DR), glaucoma (GLC), and age-related macular degeneration (AMD). The proposed method is composed of two steps. First, an initial quality evaluation in the classification system is proposed to filter out poorquality images to enhance its performance, a technique that has not been explored previously. Second, the transfer learning technique is used with various convolutional neural networks (CNN) models that automatically learn a thousand features in the digital retinal image, and are based on those features for diagnosing eye diseases. Comparison performance of many models is conducted to find the optimal model which fits with fundus classification. Among the different CNN models, DenseNet-201 outperforms others with an area under the receiver operating characteristic curve of 0.99. Furthermore, the corresponding specificities for healthy, DR, GLC, and AMD patients are found to be 89.52%, 96.69%, 89.58%, and 100%, respectively. These results demonstrate that the proposed method can reduce the time-consumption by automatically diagnosing multiple eye diseases using computer-aided assistance tools. | - |
dc.format.extent | 14 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Tech Science Press | - |
dc.title | Comparative Study of Transfer Learning Models for Retinal Disease Diagnosis from Fundus Images | - |
dc.type | Article | - |
dc.publisher.location | 미국 | - |
dc.identifier.doi | 10.32604/cmc.2022.021943 | - |
dc.identifier.scopusid | 2-s2.0-85117062078 | - |
dc.identifier.wosid | 000707364500011 | - |
dc.identifier.bibliographicCitation | Computers, Materials and Continua, v.70, no.3, pp 5821 - 5834 | - |
dc.citation.title | Computers, Materials and Continua | - |
dc.citation.volume | 70 | - |
dc.citation.number | 3 | - |
dc.citation.startPage | 5821 | - |
dc.citation.endPage | 5834 | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Materials Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Materials Science, Multidisciplinary | - |
dc.subject.keywordPlus | CONVOLUTIONAL NEURAL-NETWORKS | - |
dc.subject.keywordPlus | DIABETIC-RETINOPATHY | - |
dc.subject.keywordPlus | CLASSIFICATION | - |
dc.subject.keywordAuthor | Multiclass classification | - |
dc.subject.keywordAuthor | deep neural networks | - |
dc.subject.keywordAuthor | glaucoma | - |
dc.subject.keywordAuthor | age-related macular degeneration | - |
dc.subject.keywordAuthor | diabetic retinopathy | - |
dc.subject.keywordAuthor | transfer learning | - |
dc.subject.keywordAuthor | qual-ity evaluation | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(31538) 22, Soonchunhyang-ro, Asan-si, Chungcheongnam-do, Republic of Korea+82-41-530-1114
COPYRIGHT 2021 by SOONCHUNHYANG UNIVERSITY ALL RIGHTS RESERVED.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.