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...
| 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. |
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| Format: | Conference or Workshop Item |
| Institution: | Universiti Teknologi Petronas |
| Record Id / ISBN-0: | utp-eprints.29160 / |
| Published: |
Institute of Electrical and Electronics Engineers Inc.
2021
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| 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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| Summary: |
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. |
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