Identification of Image Features for Skin Burn Depth Classification

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Bibliographic Details
Main Author:	Kuan, Pei Nei
Format:	Thesis
Language:	English
Published:	2018
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://ir.unimas.my/id/eprint/24891/3/Kuan.pdf
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id	my-unimas-ir.24891
record_format	uketd_dc
spelling	my-unimas-ir.248912023-11-14T01:30:20Z Identification of Image Features for Skin Burn Depth Classification 2018 Kuan, Pei Nei QA75 Electronic computers. Computer science Universiti Malaysia Sarawak, (UNIMAS) 2018 Thesis http://ir.unimas.my/id/eprint/24891/ http://ir.unimas.my/id/eprint/24891/3/Kuan.pdf text en validuser masters Universiti Malaysia Sarawak Faculty of Computer Science and Information Technology
institution	Universiti Malaysia Sarawak
collection	UNIMAS Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Kuan, Pei Nei Identification of Image Features for Skin Burn Depth Classification
description
format	Thesis
qualification_level	Master's degree
author	Kuan, Pei Nei
author_facet	Kuan, Pei Nei
author_sort	Kuan, Pei Nei
title	Identification of Image Features for Skin Burn Depth Classification
title_short	Identification of Image Features for Skin Burn Depth Classification
title_full	Identification of Image Features for Skin Burn Depth Classification
title_fullStr	Identification of Image Features for Skin Burn Depth Classification
title_full_unstemmed	Identification of Image Features for Skin Burn Depth Classification
title_sort	identification of image features for skin burn depth classification
granting_institution	Universiti Malaysia Sarawak
granting_department	Faculty of Computer Science and Information Technology
publishDate	2018
url	http://ir.unimas.my/id/eprint/24891/3/Kuan.pdf
_version_	1783728281945636864

Identification of Image Features for Skin Burn Depth Classification

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