Diagnosis of retinal disorders from Optical Coherence Tomography images using CNN.

An efficient automatic decision support system for detection of retinal disorders is important and is the need of the hour. Optical Coherence Tomography (OCT) is the current imaging modality for the early detection of retinal disorders non-invasively. In this work, a Convolution Neural Network (CNN)...

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Autores principales:	Nithya Rajagopalan, Venkateswaran N, Alex Noel Josephraj, Srithaladevi E
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/62f743ab403947f9a62f4e5a26e9c4b9
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id	oai:doaj.org-article:62f743ab403947f9a62f4e5a26e9c4b9
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spelling	oai:doaj.org-article:62f743ab403947f9a62f4e5a26e9c4b92021-12-02T20:06:25ZDiagnosis of retinal disorders from Optical Coherence Tomography images using CNN.1932-620310.1371/journal.pone.0254180https://doaj.org/article/62f743ab403947f9a62f4e5a26e9c4b92021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0254180https://doaj.org/toc/1932-6203An efficient automatic decision support system for detection of retinal disorders is important and is the need of the hour. Optical Coherence Tomography (OCT) is the current imaging modality for the early detection of retinal disorders non-invasively. In this work, a Convolution Neural Network (CNN) model is proposed to classify three types of retinal disorders namely: Choroidal neovascularization (CNV), Drusen macular degeneration (DMD) and Diabetic macular edema (DME). The hyperparameters of the model like batch size, number of epochs, dropout rate, and the type of optimizer are tuned using random search optimization method for better performance to classify different retinal disorders. The proposed architecture provides an accuracy of 97.01%, sensitivity of 93.43%, and 98.07% specificity and it outperformed other existing models, when compared. The proposed model can be used for the large-scale screening of retinal disorders effectively.Nithya RajagopalanVenkateswaran NAlex Noel JosephrajSrithaladevi EPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 7, p e0254180 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Nithya Rajagopalan Venkateswaran N Alex Noel Josephraj Srithaladevi E Diagnosis of retinal disorders from Optical Coherence Tomography images using CNN.
description	An efficient automatic decision support system for detection of retinal disorders is important and is the need of the hour. Optical Coherence Tomography (OCT) is the current imaging modality for the early detection of retinal disorders non-invasively. In this work, a Convolution Neural Network (CNN) model is proposed to classify three types of retinal disorders namely: Choroidal neovascularization (CNV), Drusen macular degeneration (DMD) and Diabetic macular edema (DME). The hyperparameters of the model like batch size, number of epochs, dropout rate, and the type of optimizer are tuned using random search optimization method for better performance to classify different retinal disorders. The proposed architecture provides an accuracy of 97.01%, sensitivity of 93.43%, and 98.07% specificity and it outperformed other existing models, when compared. The proposed model can be used for the large-scale screening of retinal disorders effectively.
format	article
author	Nithya Rajagopalan Venkateswaran N Alex Noel Josephraj Srithaladevi E
author_facet	Nithya Rajagopalan Venkateswaran N Alex Noel Josephraj Srithaladevi E
author_sort	Nithya Rajagopalan
title	Diagnosis of retinal disorders from Optical Coherence Tomography images using CNN.
title_short	Diagnosis of retinal disorders from Optical Coherence Tomography images using CNN.
title_full	Diagnosis of retinal disorders from Optical Coherence Tomography images using CNN.
title_fullStr	Diagnosis of retinal disorders from Optical Coherence Tomography images using CNN.
title_full_unstemmed	Diagnosis of retinal disorders from Optical Coherence Tomography images using CNN.
title_sort	diagnosis of retinal disorders from optical coherence tomography images using cnn.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/62f743ab403947f9a62f4e5a26e9c4b9
work_keys_str_mv	AT nithyarajagopalan diagnosisofretinaldisordersfromopticalcoherencetomographyimagesusingcnn AT venkateswarann diagnosisofretinaldisordersfromopticalcoherencetomographyimagesusingcnn AT alexnoeljosephraj diagnosisofretinaldisordersfromopticalcoherencetomographyimagesusingcnn AT srithaladevie diagnosisofretinaldisordersfromopticalcoherencetomographyimagesusingcnn
_version_	1718375362364178432

Diagnosis of retinal disorders from Optical Coherence Tomography images using CNN.

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