Big Data and Artificial Intelligence (AI) to Detect Glaucoma

Yang, L; Xie, Y; Islam, MR; Xu, G

Big Data and Artificial Intelligence (AI) to Detect Glaucoma

Yang, L Xie, Y Islam, MR Xu, G

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Conference Proceeding
Citation:: Proceedings of the 2022 IEEE International Conference on Behavioural and Social Computing, BESC 2022, 2022, 00, pp. 1-6
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Yang, L
dc.contributor.author	Xie, Y
dc.contributor.author	Islam, MR
dc.contributor.author	Xu, G https://orcid.org/0000-0003-4493-6663
dc.date	2022-10-29
dc.date.accessioned	2023-04-18T02:04:38Z
dc.date.available	2023-04-18T02:04:38Z
dc.date.issued	2022-01-01
dc.identifier.citation	Proceedings of the 2022 IEEE International Conference on Behavioural and Social Computing, BESC 2022, 2022, 00, pp. 1-6
dc.identifier.isbn	9798350398144
dc.identifier.uri	http://hdl.handle.net/10453/169893
dc.description.abstract	Glaucoma is an eye condition that is one of the most prevalent causes of blindness due to damage to the optic nerve. According to existing studies, it is the second most common reason for vision loss worldwide. With the advanced development of artificial intelligence (AI), it is necessary to develop a glaucoma detection system for diagnosis. Therefore, the primary objective of this paper is to create a system for detecting glaucoma using retinal fundus images, which can help determine if the patient was affected by glaucoma. Although several methods have been applied to detect glaucoma in the past decades, it is essential to use an advanced AI technique with a glaucoma detection system. Thus, in this paper, we divided our task into threefold: 1) segmentation, 2) classification, and 3) deployment. The U-Net architecture is implemented for segmentation. The pretrained GC-Net model is proposed for classification. Finally, based on the segmentation and classification, we developed a glaucoma detection system for diagnosis. This study uses ACRIMA datasets for training and testing. The result of this model is evaluated using various parameters such as accuracy, sensitivity, specificity, f1-score, and auc score. The output is compared to deep learning models such as ResNet, CNN, Inception V3, and TB-Net. The proposed model achieved 96% accuracy in training and 93% accuracy in testing. Overall, the performance of the proposed model is better in all the analyses.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	Proceedings of the 2022 IEEE International Conference on Behavioural and Social Computing, BESC 2022
dc.relation.ispartof	2022 9th International Conference on Behavioural and Social Computing (BESC)
dc.relation.isbasedon	10.1109/BESC57393.2022.9994958
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Big Data and Artificial Intelligence (AI) to Detect Glaucoma
dc.type	Conference Proceeding
utslib.citation.volume	00
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2023-04-18T02:04:37Z
pubs.finish-date	2022-10-31
pubs.publication-status	Published
pubs.start-date	2022-10-29
pubs.volume	00

Abstract:

Glaucoma is an eye condition that is one of the most prevalent causes of blindness due to damage to the optic nerve. According to existing studies, it is the second most common reason for vision loss worldwide. With the advanced development of artificial intelligence (AI), it is necessary to develop a glaucoma detection system for diagnosis. Therefore, the primary objective of this paper is to create a system for detecting glaucoma using retinal fundus images, which can help determine if the patient was affected by glaucoma. Although several methods have been applied to detect glaucoma in the past decades, it is essential to use an advanced AI technique with a glaucoma detection system. Thus, in this paper, we divided our task into threefold: 1) segmentation, 2) classification, and 3) deployment. The U-Net architecture is implemented for segmentation. The pretrained GC-Net model is proposed for classification. Finally, based on the segmentation and classification, we developed a glaucoma detection system for diagnosis. This study uses ACRIMA datasets for training and testing. The result of this model is evaluated using various parameters such as accuracy, sensitivity, specificity, f1-score, and auc score. The output is compared to deep learning models such as ResNet, CNN, Inception V3, and TB-Net. The proposed model achieved 96% accuracy in training and 93% accuracy in testing. Overall, the performance of the proposed model is better in all the analyses.

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http://hdl.handle.net/10453/169893