Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2021 Jun 16;18(5):5321-5346.
doi: 10.3934/mbe.2021270.

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

Aziz Ur Rehman  1 , Imtiaz A Taj  2 , Muhammad Sajid  3 , Khasan S Karimov  1   4
Affiliations
Free article

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

Aziz Ur Rehman et al. Math Biosci Eng. .
Free article

Abstract

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.

Keywords: Deep Convolutional Neural Network; Optic Nerve Head; accuracy based weighted voting and averaging; ensemble; fundoscopy; performance metrics; transfer learning.

PubMed Disclaimer

Similar articles

See all similar articles

Cited by