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An AMDOCT-NET for Automated AMD Detection under Evaluations of Different Image Size, Denoising and Cropping

This paper proposed a novel deep learning architecture, called the AMDOCT-NET architecture, to accurately detect age-related macular degeneration (AMD) on optical coherence tomography (OCT) images. Using the AMDOCT-NET architecture, the performance of various image processing, such as resizing, deno...

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Main Authors: You, H.-Y., Wei, H.-T., Lin, C.-H., Ji, J.-Y., Liu, Y.-H., Lu, C.-K., Wang, J.-K., Huang, T.-L.
Format: Conference or Workshop Item
Published: Institute of Electrical and Electronics Engineers Inc. 2021
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124889225&doi=10.1109%2fECBIOS51820.2021.9510570&partnerID=40&md5=f389d9d943d6b71fe6cad8b443328a64
http://eprints.utp.edu.my/29160/
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spelling my.utp.eprints.291602022-03-25T01:03:53Z An AMDOCT-NET for Automated AMD Detection under Evaluations of Different Image Size, Denoising and Cropping You, H.-Y. Wei, H.-T. Lin, C.-H. Ji, J.-Y. Liu, Y.-H. Lu, C.-K. Wang, J.-K. Huang, T.-L. This paper proposed a novel deep learning architecture, called the AMDOCT-NET architecture, to accurately detect age-related macular degeneration (AMD) on optical coherence tomography (OCT) images. Using the AMDOCT-NET architecture, the performance of various image processing, such as resizing, denoising, and cropping has been evaluated. The simulation results show that the AMDOCT-NET architecture with an input size of 224�224 pixels, no cropping, and no denoising achieves the accuracy of 99.09 to automatically detect the AMD. Compared with the well-known deep learning architecture, VGG16, the AMDOCT-NET improves accuracy by 2.09 and reduces the model size by 53.7. © 2021 ECBIOS 2021. All rights reserved. Institute of Electrical and Electronics Engineers Inc. 2021 Conference or Workshop Item NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124889225&doi=10.1109%2fECBIOS51820.2021.9510570&partnerID=40&md5=f389d9d943d6b71fe6cad8b443328a64 You, H.-Y. and Wei, H.-T. and Lin, C.-H. and Ji, J.-Y. and Liu, Y.-H. and Lu, C.-K. and Wang, J.-K. and Huang, T.-L. (2021) An AMDOCT-NET for Automated AMD Detection under Evaluations of Different Image Size, Denoising and Cropping. In: UNSPECIFIED. http://eprints.utp.edu.my/29160/
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description This paper proposed a novel deep learning architecture, called the AMDOCT-NET architecture, to accurately detect age-related macular degeneration (AMD) on optical coherence tomography (OCT) images. Using the AMDOCT-NET architecture, the performance of various image processing, such as resizing, denoising, and cropping has been evaluated. The simulation results show that the AMDOCT-NET architecture with an input size of 224�224 pixels, no cropping, and no denoising achieves the accuracy of 99.09 to automatically detect the AMD. Compared with the well-known deep learning architecture, VGG16, the AMDOCT-NET improves accuracy by 2.09 and reduces the model size by 53.7. © 2021 ECBIOS 2021. All rights reserved.
format Conference or Workshop Item
author You, H.-Y.
Wei, H.-T.
Lin, C.-H.
Ji, J.-Y.
Liu, Y.-H.
Lu, C.-K.
Wang, J.-K.
Huang, T.-L.
spellingShingle You, H.-Y.
Wei, H.-T.
Lin, C.-H.
Ji, J.-Y.
Liu, Y.-H.
Lu, C.-K.
Wang, J.-K.
Huang, T.-L.
An AMDOCT-NET for Automated AMD Detection under Evaluations of Different Image Size, Denoising and Cropping
author_facet You, H.-Y.
Wei, H.-T.
Lin, C.-H.
Ji, J.-Y.
Liu, Y.-H.
Lu, C.-K.
Wang, J.-K.
Huang, T.-L.
author_sort You, H.-Y.
title An AMDOCT-NET for Automated AMD Detection under Evaluations of Different Image Size, Denoising and Cropping
title_short An AMDOCT-NET for Automated AMD Detection under Evaluations of Different Image Size, Denoising and Cropping
title_full An AMDOCT-NET for Automated AMD Detection under Evaluations of Different Image Size, Denoising and Cropping
title_fullStr An AMDOCT-NET for Automated AMD Detection under Evaluations of Different Image Size, Denoising and Cropping
title_full_unstemmed An AMDOCT-NET for Automated AMD Detection under Evaluations of Different Image Size, Denoising and Cropping
title_sort amdoct-net for automated amd detection under evaluations of different image size, denoising and cropping
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2021
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124889225&doi=10.1109%2fECBIOS51820.2021.9510570&partnerID=40&md5=f389d9d943d6b71fe6cad8b443328a64
http://eprints.utp.edu.my/29160/
_version_ 1738656926498029568
score 13.160551

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