An ensemble framework based on Deep CNNs architecture for glaucoma classification using fundus photography

Glaucoma is a chronic ocular degenerative disease that can cause blindness if left untreated in its early stages. Deep Convolutional Neural Networks (Deep CNNs) and its variants have provided superior performance in glaucoma classification, segmentation, and detection. In this paper, we propose a tw...

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Autores principales:	Aziz-ur-Rehman, Imtiaz A. Taj, Muhammad Sajid, Khasan S. Karimov
Formato:	article
Lenguaje:	EN
Publicado:	AIMS Press 2021
Materias:	deep convolutional neural network transfer learning fundoscopy optic nerve head performance metrics ensemble accuracy based weighted voting and averaging Biotechnology TP248.13-248.65 Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/30cda7b95ecb4736abc05e3f7d820abd
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spelling	oai:doaj.org-article:30cda7b95ecb4736abc05e3f7d820abd2021-11-09T02:05:17ZAn ensemble framework based on Deep CNNs architecture for glaucoma classification using fundus photography10.3934/mbe.20212701551-0018https://doaj.org/article/30cda7b95ecb4736abc05e3f7d820abd2021-06-01T00:00:00Zhttps://www.aimspress.com/article/doi/10.3934/mbe.2021270?viewType=HTMLhttps://doaj.org/toc/1551-0018Glaucoma is a chronic ocular degenerative disease that can cause blindness if left untreated in its early stages. Deep Convolutional Neural Networks (Deep CNNs) and its variants have provided superior performance in glaucoma classification, segmentation, and detection. In this paper, we propose a two-staged glaucoma classification scheme based on Deep CNN architectures. In stage one, four different ImageNet pre-trained Deep CNN architectures, i.e., AlexNet, InceptionV3, InceptionResNetV2, and NasNet-Large are used and it is observed that NasNet-Large architecture provides superior performance in terms of sensitivity (99.1%), specificity (99.4%), accuracy (99.3%), and area under the receiver operating characteristic curve (97.8%) metrics. A detailed performance comparison is also presented among these on public datasets, i.e., ACRIMA, ORIGA-Light, and RIM-ONE as well as locally available datasets, i.e., AFIO, and HMC. In the second stage, we propose an ensemble classifier with two novel ensembling techniques, i.e., accuracy based weighted voting, and accuracy/score based weighted averaging to further improve the glaucoma classification results. It is shown that ensemble with accuracy/score based scheme improves the accuracy (99.5%) for diverse databases. As an outcome of this study, it is presented that the NasNet-Large architecture is a feasible option in terms of its performance as a single classifier while ensemble classifier further improves the generalized performance for automatic glaucoma classification.Aziz-ur-RehmanImtiaz A. TajMuhammad SajidKhasan S. KarimovAIMS Pressarticledeep convolutional neural networktransfer learningfundoscopyoptic nerve headperformance metricsensembleaccuracy based weighted voting and averagingBiotechnologyTP248.13-248.65MathematicsQA1-939ENMathematical Biosciences and Engineering, Vol 18, Iss 5, Pp 5321-5346 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	deep convolutional neural network transfer learning fundoscopy optic nerve head performance metrics ensemble accuracy based weighted voting and averaging Biotechnology TP248.13-248.65 Mathematics QA1-939
spellingShingle	deep convolutional neural network transfer learning fundoscopy optic nerve head performance metrics ensemble accuracy based weighted voting and averaging Biotechnology TP248.13-248.65 Mathematics QA1-939 Aziz-ur-Rehman Imtiaz A. Taj Muhammad Sajid Khasan S. Karimov An ensemble framework based on Deep CNNs architecture for glaucoma classification using fundus photography
description	Glaucoma is a chronic ocular degenerative disease that can cause blindness if left untreated in its early stages. Deep Convolutional Neural Networks (Deep CNNs) and its variants have provided superior performance in glaucoma classification, segmentation, and detection. In this paper, we propose a two-staged glaucoma classification scheme based on Deep CNN architectures. In stage one, four different ImageNet pre-trained Deep CNN architectures, i.e., AlexNet, InceptionV3, InceptionResNetV2, and NasNet-Large are used and it is observed that NasNet-Large architecture provides superior performance in terms of sensitivity (99.1%), specificity (99.4%), accuracy (99.3%), and area under the receiver operating characteristic curve (97.8%) metrics. A detailed performance comparison is also presented among these on public datasets, i.e., ACRIMA, ORIGA-Light, and RIM-ONE as well as locally available datasets, i.e., AFIO, and HMC. In the second stage, we propose an ensemble classifier with two novel ensembling techniques, i.e., accuracy based weighted voting, and accuracy/score based weighted averaging to further improve the glaucoma classification results. It is shown that ensemble with accuracy/score based scheme improves the accuracy (99.5%) for diverse databases. As an outcome of this study, it is presented that the NasNet-Large architecture is a feasible option in terms of its performance as a single classifier while ensemble classifier further improves the generalized performance for automatic glaucoma classification.
format	article
author	Aziz-ur-Rehman Imtiaz A. Taj Muhammad Sajid Khasan S. Karimov
author_facet	Aziz-ur-Rehman Imtiaz A. Taj Muhammad Sajid Khasan S. Karimov
author_sort	Aziz-ur-Rehman
title	An ensemble framework based on Deep CNNs architecture for glaucoma classification using fundus photography
title_short	An ensemble framework based on Deep CNNs architecture for glaucoma classification using fundus photography
title_full	An ensemble framework based on Deep CNNs architecture for glaucoma classification using fundus photography
title_fullStr	An ensemble framework based on Deep CNNs architecture for glaucoma classification using fundus photography
title_full_unstemmed	An ensemble framework based on Deep CNNs architecture for glaucoma classification using fundus photography
title_sort	ensemble framework based on deep cnns architecture for glaucoma classification using fundus photography
publisher	AIMS Press
publishDate	2021
url	https://doaj.org/article/30cda7b95ecb4736abc05e3f7d820abd
work_keys_str_mv	AT azizurrehman anensembleframeworkbasedondeepcnnsarchitectureforglaucomaclassificationusingfundusphotography AT imtiazataj anensembleframeworkbasedondeepcnnsarchitectureforglaucomaclassificationusingfundusphotography AT muhammadsajid anensembleframeworkbasedondeepcnnsarchitectureforglaucomaclassificationusingfundusphotography AT khasanskarimov anensembleframeworkbasedondeepcnnsarchitectureforglaucomaclassificationusingfundusphotography AT azizurrehman ensembleframeworkbasedondeepcnnsarchitectureforglaucomaclassificationusingfundusphotography AT imtiazataj ensembleframeworkbasedondeepcnnsarchitectureforglaucomaclassificationusingfundusphotography AT muhammadsajid ensembleframeworkbasedondeepcnnsarchitectureforglaucomaclassificationusingfundusphotography AT khasanskarimov ensembleframeworkbasedondeepcnnsarchitectureforglaucomaclassificationusingfundusphotography
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An ensemble framework based on Deep CNNs architecture for glaucoma classification using fundus photography

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