Advances in the diagnosis of herpes simplex stromal necrotising keratitis: A feasibility study on deep learning approach

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DC Field	Value	Language
dc.contributor.author	Raman, Sundaresan	-
dc.date.accessioned	2024-11-11T09:49:42Z	-
dc.date.available	2024-11-11T09:49:42Z	-
dc.date.issued	2022-09	-
dc.identifier.uri	https://journals.lww.com/ijo/fulltext/2022/09000/Advances_in_the_diagnosis_of_herpes_simplex.24.aspx?context=LatestArticles	-
dc.identifier.uri	http://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/16323	-
dc.description.abstract	Infectious keratitis, especially viral keratitis (VK), in resource-limited settings, can be a challenge to diagnose and carries a high risk of misdiagnosis contributing to significant ocular morbidity. We aimed to employ and study the application of artificial intelligence-based deep learning (DL) algorithms to diagnose VK.	en_US
dc.language.iso	en	en_US
dc.publisher	Wolters Kluwer	en_US
dc.subject	Computer Science	en_US
dc.subject	Deep Learning (DL)	en_US
dc.subject	Necrotising keratitis	en_US
dc.title	Advances in the diagnosis of herpes simplex stromal necrotising keratitis: A feasibility study on deep learning approach	en_US
dc.type	Article	en_US
Appears in Collections:	Department of Computer Science and Information Systems

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