A Novel Deep Learning Framework for Enhanced Acute Lymphoblastic Leukemia Detection

Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/4723

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DC Field	Value	Language
dc.contributor.author	Ahmadsaidulu, Shaik	-
dc.contributor.author	Kanase, Akash Suresh	-
dc.contributor.author	Jain, Puneet Kumar	-
dc.contributor.author	Banoth, Earu	-
dc.date.accessioned	2024-11-03T11:30:39Z	-
dc.date.available	2024-11-03T11:30:39Z	-
dc.date.issued	2024-09	-
dc.identifier.citation	Frontiers in Optics and Laser Science Conference (FiO LS-2024), Colorado Convention Center, Denver, Colorado, USA, 22–26 September 2024	en_US
dc.identifier.uri	http://hdl.handle.net/2080/4723	-
dc.description	Copyright belongs to proceeding publisher	en_US
dc.description.abstract	In this work a deep-learning model using enhanced YOLOv8(You Only Look Once) for classifying Acute Lymphoblastic Leukemia (ALL) and other normal cells. Achieving 98% accuracy for ALL and 91% for combined (ALL & Normal) classification enhances clinical decision-making.	en_US
dc.subject	Deep Learning	en_US
dc.subject	Acute Lymphoblastic Leukemia	en_US
dc.title	A Novel Deep Learning Framework for Enhanced Acute Lymphoblastic Leukemia Detection	en_US
dc.type	Presentation	en_US
Appears in Collections:	Conference Papers

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