Slice: Combined Super Linear Iterative Clustering and Earth Mover’S Distance for Brain Tumour Classification

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DC Field	Value	Language
dc.contributor.author	Neelamraju, Pavan Mohan	-
dc.contributor.author	Muniraj, Inbarasan	-
dc.date.accessioned	2024-12-12T09:29:58Z	-
dc.date.available	2024-12-12T09:29:58Z	-
dc.date.issued	2024	-
dc.identifier.isbn	9781957171371	-
dc.identifier.uri	https://opg.optica.org/abstract.cfm?URI=3D-2024-DW1H.3	-
dc.identifier.uri	https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/16763	-
dc.description.abstract	We demonstrate that the combination of Super Linear Iterative Clustering and Earth Mover’s Distance efficiently segments tumours from the MRI dataset. Despite using a smaller training dataset our approach achieves an accuracy of 86.2%. © 2024 The Author(s).	en_US
dc.language.iso	en	en_US
dc.publisher	Optica Imaging Congress 2024 (3D, AOMS, COSI, ISA, pcAOP)	en_US
dc.publisher	Optical Society of America	en_US
dc.subject	Magnetic Resonance Imaging	en_US
dc.subject	Brain Tumor Classifications	en_US
dc.subject	Iterative Clustering	en_US
dc.subject	Small Training	en_US
dc.subject	Training Dataset	en_US
dc.title	Slice: Combined Super Linear Iterative Clustering and Earth Mover’S Distance for Brain Tumour Classification	en_US
dc.type	Article	en_US
Appears in Collections:	Conference Papers

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