A Novel Context Aware Joint Segmentation and Classification Framework for Glaucoma Detection

Glaucoma is a chronic ocular disease characterized by damage to the optic nerve resulting in progressive and irreversible visual loss. Early detection and timely clinical interventions are critical in improving glaucoma-related outcomes. As a typical and complicated ocular disease, glaucoma detectio...

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Autores principales:	S. Sankar Ganesh, G. Kannayeram, Alagar Karthick, M. Muhibbullah
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/caf926e6cd5b4103b61fb74090467b00
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spelling	oai:doaj.org-article:caf926e6cd5b4103b61fb74090467b002021-11-15T01:19:24ZA Novel Context Aware Joint Segmentation and Classification Framework for Glaucoma Detection1748-671810.1155/2021/2921737https://doaj.org/article/caf926e6cd5b4103b61fb74090467b002021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/2921737https://doaj.org/toc/1748-6718Glaucoma is a chronic ocular disease characterized by damage to the optic nerve resulting in progressive and irreversible visual loss. Early detection and timely clinical interventions are critical in improving glaucoma-related outcomes. As a typical and complicated ocular disease, glaucoma detection presents a unique challenge due to its insidious onset and high intra- and interpatient variabilities. Recent studies have demonstrated that robust glaucoma detection systems can be realized with deep learning approaches. The optic disc (OD) is the most commonly studied retinal structure for screening and diagnosing glaucoma. This paper proposes a novel context aware deep learning framework called GD-YNet, for OD segmentation and glaucoma detection. It leverages the potential of aggregated transformations and the simplicity of the YNet architecture in context aware OD segmentation and binary classification for glaucoma detection. Trained with the RIGA and RIMOne-V2 datasets, this model achieves glaucoma detection accuracies of 99.72%, 98.02%, 99.50%, and 99.41% with the ACRIMA, Drishti-gs, REFUGE, and RIMOne-V1 datasets. Further, the proposed model can be extended to a multiclass segmentation and classification model for glaucoma staging and severity assessment.S. Sankar GaneshG. KannayeramAlagar KarthickM. MuhibbullahHindawi LimitedarticleComputer applications to medicine. Medical informaticsR858-859.7ENComputational and Mathematical Methods in Medicine, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 S. Sankar Ganesh G. Kannayeram Alagar Karthick M. Muhibbullah A Novel Context Aware Joint Segmentation and Classification Framework for Glaucoma Detection
description	Glaucoma is a chronic ocular disease characterized by damage to the optic nerve resulting in progressive and irreversible visual loss. Early detection and timely clinical interventions are critical in improving glaucoma-related outcomes. As a typical and complicated ocular disease, glaucoma detection presents a unique challenge due to its insidious onset and high intra- and interpatient variabilities. Recent studies have demonstrated that robust glaucoma detection systems can be realized with deep learning approaches. The optic disc (OD) is the most commonly studied retinal structure for screening and diagnosing glaucoma. This paper proposes a novel context aware deep learning framework called GD-YNet, for OD segmentation and glaucoma detection. It leverages the potential of aggregated transformations and the simplicity of the YNet architecture in context aware OD segmentation and binary classification for glaucoma detection. Trained with the RIGA and RIMOne-V2 datasets, this model achieves glaucoma detection accuracies of 99.72%, 98.02%, 99.50%, and 99.41% with the ACRIMA, Drishti-gs, REFUGE, and RIMOne-V1 datasets. Further, the proposed model can be extended to a multiclass segmentation and classification model for glaucoma staging and severity assessment.
format	article
author	S. Sankar Ganesh G. Kannayeram Alagar Karthick M. Muhibbullah
author_facet	S. Sankar Ganesh G. Kannayeram Alagar Karthick M. Muhibbullah
author_sort	S. Sankar Ganesh
title	A Novel Context Aware Joint Segmentation and Classification Framework for Glaucoma Detection
title_short	A Novel Context Aware Joint Segmentation and Classification Framework for Glaucoma Detection
title_full	A Novel Context Aware Joint Segmentation and Classification Framework for Glaucoma Detection
title_fullStr	A Novel Context Aware Joint Segmentation and Classification Framework for Glaucoma Detection
title_full_unstemmed	A Novel Context Aware Joint Segmentation and Classification Framework for Glaucoma Detection
title_sort	novel context aware joint segmentation and classification framework for glaucoma detection
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/caf926e6cd5b4103b61fb74090467b00
work_keys_str_mv	AT ssankarganesh anovelcontextawarejointsegmentationandclassificationframeworkforglaucomadetection AT gkannayeram anovelcontextawarejointsegmentationandclassificationframeworkforglaucomadetection AT alagarkarthick anovelcontextawarejointsegmentationandclassificationframeworkforglaucomadetection AT mmuhibbullah anovelcontextawarejointsegmentationandclassificationframeworkforglaucomadetection AT ssankarganesh novelcontextawarejointsegmentationandclassificationframeworkforglaucomadetection AT gkannayeram novelcontextawarejointsegmentationandclassificationframeworkforglaucomadetection AT alagarkarthick novelcontextawarejointsegmentationandclassificationframeworkforglaucomadetection AT mmuhibbullah novelcontextawarejointsegmentationandclassificationframeworkforglaucomadetection
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A Novel Context Aware Joint Segmentation and Classification Framework for Glaucoma Detection

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