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Evaluations of Deep Learning Approaches for Glaucoma Screening Using Retinal Images from Mobile Device

Glaucoma is a silent disease that leads to vision loss or irreversible blindness. Current deep learning methods can help glaucoma screening by extending it to larger populations using retinal images. Low-cost lenses attached to mobile devices can increase the frequency of screening and alert patient...

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Detalles Bibliográficos
Autores principales: Neto, Alexandre, Camara, José, Cunha, António
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8874723/
https://www.ncbi.nlm.nih.gov/pubmed/35214351
http://dx.doi.org/10.3390/s22041449
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author Neto, Alexandre
Camara, José
Cunha, António
author_facet Neto, Alexandre
Camara, José
Cunha, António
author_sort Neto, Alexandre
collection PubMed
description Glaucoma is a silent disease that leads to vision loss or irreversible blindness. Current deep learning methods can help glaucoma screening by extending it to larger populations using retinal images. Low-cost lenses attached to mobile devices can increase the frequency of screening and alert patients earlier for a more thorough evaluation. This work explored and compared the performance of classification and segmentation methods for glaucoma screening with retinal images acquired by both retinography and mobile devices. The goal was to verify the results of these methods and see if similar results could be achieved using images captured by mobile devices. The used classification methods were the Xception, ResNet152 V2 and the Inception ResNet V2 models. The models’ activation maps were produced and analysed to support glaucoma classifier predictions. In clinical practice, glaucoma assessment is commonly based on the cup-to-disc ratio (CDR) criterion, a frequent indicator used by specialists. For this reason, additionally, the U-Net architecture was used with the Inception ResNet V2 and Inception V3 models as the backbone to segment and estimate CDR. For both tasks, the performance of the models reached close to that of state-of-the-art methods, and the classification method applied to a low-quality private dataset illustrates the advantage of using cheaper lenses.
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spelling pubmed-88747232022-02-26 Evaluations of Deep Learning Approaches for Glaucoma Screening Using Retinal Images from Mobile Device Neto, Alexandre Camara, José Cunha, António Sensors (Basel) Article Glaucoma is a silent disease that leads to vision loss or irreversible blindness. Current deep learning methods can help glaucoma screening by extending it to larger populations using retinal images. Low-cost lenses attached to mobile devices can increase the frequency of screening and alert patients earlier for a more thorough evaluation. This work explored and compared the performance of classification and segmentation methods for glaucoma screening with retinal images acquired by both retinography and mobile devices. The goal was to verify the results of these methods and see if similar results could be achieved using images captured by mobile devices. The used classification methods were the Xception, ResNet152 V2 and the Inception ResNet V2 models. The models’ activation maps were produced and analysed to support glaucoma classifier predictions. In clinical practice, glaucoma assessment is commonly based on the cup-to-disc ratio (CDR) criterion, a frequent indicator used by specialists. For this reason, additionally, the U-Net architecture was used with the Inception ResNet V2 and Inception V3 models as the backbone to segment and estimate CDR. For both tasks, the performance of the models reached close to that of state-of-the-art methods, and the classification method applied to a low-quality private dataset illustrates the advantage of using cheaper lenses. MDPI 2022-02-14 /pmc/articles/PMC8874723/ /pubmed/35214351 http://dx.doi.org/10.3390/s22041449 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Neto, Alexandre
Camara, José
Cunha, António
Evaluations of Deep Learning Approaches for Glaucoma Screening Using Retinal Images from Mobile Device
title Evaluations of Deep Learning Approaches for Glaucoma Screening Using Retinal Images from Mobile Device
title_full Evaluations of Deep Learning Approaches for Glaucoma Screening Using Retinal Images from Mobile Device
title_fullStr Evaluations of Deep Learning Approaches for Glaucoma Screening Using Retinal Images from Mobile Device
title_full_unstemmed Evaluations of Deep Learning Approaches for Glaucoma Screening Using Retinal Images from Mobile Device
title_short Evaluations of Deep Learning Approaches for Glaucoma Screening Using Retinal Images from Mobile Device
title_sort evaluations of deep learning approaches for glaucoma screening using retinal images from mobile device
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8874723/
https://www.ncbi.nlm.nih.gov/pubmed/35214351
http://dx.doi.org/10.3390/s22041449
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