Comparative Analysis of Deep Learning Architectures for Macular Hole Segmentation in OCT Images: A P...
Comparative Analysis of Deep Learning Architectures for Macular Hole Segmentation in OCT Images: A Performance Evaluation of U-Net Variants
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Switzerland: MDPI AG
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English
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Switzerland: MDPI AG
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This study presents a comprehensive comparison of U-Net variants with different backbone architectures for Macular Hole (MH) segmentation in optical coherence tomography (OCT) images. We evaluated eleven architectures, including U-Net combined with InceptionNetV4, VGG16, VGG19, ResNet152, DenseNet121, EfficientNet-B7, MobileNetV2, Xception, and Tra...
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Comparative Analysis of Deep Learning Architectures for Macular Hole Segmentation in OCT Images: A Performance Evaluation of U-Net Variants
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TN_cdi_doaj_primary_oai_doaj_org_article_a59fcc005e8b4e31a90e51a09d8737bf
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_a59fcc005e8b4e31a90e51a09d8737bf
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ISSN
2313-433X
E-ISSN
2313-433X
DOI
10.3390/jimaging11020053
