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Investigating size features of acute leukemia using k-nearest neighbors

The 2nd International Malaysia-Ireland Joint Symposium on Engineering, Science and Business 2012 (IMiEJS2012) jointly organized by Universiti Malaysia Perlis and Athlone Institute of Technology in collaboration with The Ministry of Higher Education (MOHE) Malaysia, Education Malaysia and Malaysia Po...

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Main Authors: Nadiatun Zawiyah, Supardi, Nor Hazlyna, Harun, Mohd Yusoff, Mashor, Prof. Dr., Rosline, Hassan, Dr.
Other Authors: nadiatun@gmail.com
Format: Working Paper
Language:English
Published: Universiti Malaysia Perlis (UniMAP) 2013
Subjects:
Data classification
Acute leukemia
K-nearest neighbor
White blood cells
Online Access:http://dspace.unimap.edu.my/xmlui/handle/123456789/30793
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spelling my.unimap-307932013-12-23T08:10:20Z Investigating size features of acute leukemia using k-nearest neighbors Nadiatun Zawiyah, Supardi Nor Hazlyna, Harun Mohd Yusoff, Mashor, Prof. Dr. Rosline, Hassan, Dr. nadiatun@gmail.com hazlyna_harun@yahoo.com yusoff@unimap.edu.my roslinehassan@gmail.com Data classification Acute leukemia K-nearest neighbor White blood cells The 2nd International Malaysia-Ireland Joint Symposium on Engineering, Science and Business 2012 (IMiEJS2012) jointly organized by Universiti Malaysia Perlis and Athlone Institute of Technology in collaboration with The Ministry of Higher Education (MOHE) Malaysia, Education Malaysia and Malaysia Postgraduates Student Association Ireland (MyPSI), 18th - 19th June 2012 at Putra World Trade Center (PWTC), Kuala Lumpur, Malaysia. This study investigates the size feature of acute leukemia of blood sample image by using k-NN as a classifier. K-NN is believed to be one of the simplest methods in classifying data including images. Hence, this paper manipulates the size feature by varying the combination of sub features. From here, the capability of size feature in classifying acute leukemia blood sample images into AML and ALL are tested. Results show that, even though the highest accuracy is not achieved by the combination of all size sub features but it still could obtain up to 98.67% of accuracy. The best accuracy obtained is 99.78% by combining radius and perimeter as an input feature. Therefore, k-NN could be applied as a classifier for this classification problem. 2013-12-23T08:10:20Z 2013-12-23T08:10:20Z 2012-06-18 Working Paper p. 743 - 749 978-967-5760-11-2 http://hdl.handle.net/123456789/30793 en Proceedings of the The 2nd International Malaysia-Ireland Joint Symposium on Engineering, Science and Business 2012 (IMiEJS2012); Universiti Malaysia Perlis (UniMAP)
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Data classification
Acute leukemia
K-nearest neighbor
White blood cells
spellingShingle Data classification
Acute leukemia
K-nearest neighbor
White blood cells
Nadiatun Zawiyah, Supardi
Nor Hazlyna, Harun
Mohd Yusoff, Mashor, Prof. Dr.
Rosline, Hassan, Dr.
Investigating size features of acute leukemia using k-nearest neighbors
description The 2nd International Malaysia-Ireland Joint Symposium on Engineering, Science and Business 2012 (IMiEJS2012) jointly organized by Universiti Malaysia Perlis and Athlone Institute of Technology in collaboration with The Ministry of Higher Education (MOHE) Malaysia, Education Malaysia and Malaysia Postgraduates Student Association Ireland (MyPSI), 18th - 19th June 2012 at Putra World Trade Center (PWTC), Kuala Lumpur, Malaysia.
author2 nadiatun@gmail.com
author_facet nadiatun@gmail.com
Nadiatun Zawiyah, Supardi
Nor Hazlyna, Harun
Mohd Yusoff, Mashor, Prof. Dr.
Rosline, Hassan, Dr.
format Working Paper
author Nadiatun Zawiyah, Supardi
Nor Hazlyna, Harun
Mohd Yusoff, Mashor, Prof. Dr.
Rosline, Hassan, Dr.
author_sort Nadiatun Zawiyah, Supardi
title Investigating size features of acute leukemia using k-nearest neighbors
title_short Investigating size features of acute leukemia using k-nearest neighbors
title_full Investigating size features of acute leukemia using k-nearest neighbors
title_fullStr Investigating size features of acute leukemia using k-nearest neighbors
title_full_unstemmed Investigating size features of acute leukemia using k-nearest neighbors
title_sort investigating size features of acute leukemia using k-nearest neighbors
publisher Universiti Malaysia Perlis (UniMAP)
publishDate 2013
url http://dspace.unimap.edu.my/xmlui/handle/123456789/30793
_version_ 1643795629099450368
score 13.222552

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