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Classification red blood cells using support vector machine

Cells; Classification (of information); Cytology; Diagnosis; Feature extraction; Image processing; Image segmentation; Imaging techniques; Medical imaging; Support vector machines; Classifier algorithms; Clinical diagnosis; Confusion matrices; Image processing technique; Mean Filte; Red blood cell;...

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Main Authors: Akrimi J.A., Suliman A., George L.E., Ahmad A.R.
Other Authors: 56728894600
Format: Conference Paper
Published: Institute of Electrical and Electronics Engineers Inc. 2023
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spelling my.uniten.dspace-223872023-05-29T14:00:40Z Classification red blood cells using support vector machine Akrimi J.A. Suliman A. George L.E. Ahmad A.R. 56728894600 25825739000 56038298400 35589598800 Cells; Classification (of information); Cytology; Diagnosis; Feature extraction; Image processing; Image segmentation; Imaging techniques; Medical imaging; Support vector machines; Classifier algorithms; Clinical diagnosis; Confusion matrices; Image processing technique; Mean Filte; Red blood cell; Retrieval systems; SVM; Blood The shape of red blood cells (RBCs) contributes to clinical diagnoses of blood diseases. The field of medical imaging has become more important because of the increasing need for automated and efficient diagnoses within a short period of time. Imaging technique plays an important role in RBC research for hematology. Classification is an important component of the retrieval system which allows one to distinguish between normal RBCs and abnormal RBCs which indicate anemia. In this paper, image processing techniques that use the optimization segmentation and mean filter play an important role in obtaining the geometric, texture and color features related to RBC images by using a photo imaging microscope. The support vector machine, which is an advanced kernel-based technique, is used to classify RBC data as either normal or abnormal, the proposed classifier algorithm achieved very good accuracy rates with validation measure of sensitivity, specificity and Kappa to be 100%, 0.998% and 0.9944 respectively. � 2014 IEEE. Final 2023-05-29T06:00:40Z 2023-05-29T06:00:40Z 2015 Conference Paper 10.1109/ICIMU.2014.7066642 2-s2.0-84937468175 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84937468175&doi=10.1109%2fICIMU.2014.7066642&partnerID=40&md5=d126f88afcdd806000a29eb33125df5f https://irepository.uniten.edu.my/handle/123456789/22387 7066642 265 269 Institute of Electrical and Electronics Engineers Inc. Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Cells; Classification (of information); Cytology; Diagnosis; Feature extraction; Image processing; Image segmentation; Imaging techniques; Medical imaging; Support vector machines; Classifier algorithms; Clinical diagnosis; Confusion matrices; Image processing technique; Mean Filte; Red blood cell; Retrieval systems; SVM; Blood
author2 56728894600
author_facet 56728894600
Akrimi J.A.
Suliman A.
George L.E.
Ahmad A.R.
format Conference Paper
author Akrimi J.A.
Suliman A.
George L.E.
Ahmad A.R.
spellingShingle Akrimi J.A.
Suliman A.
George L.E.
Ahmad A.R.
Classification red blood cells using support vector machine
author_sort Akrimi J.A.
title Classification red blood cells using support vector machine
title_short Classification red blood cells using support vector machine
title_full Classification red blood cells using support vector machine
title_fullStr Classification red blood cells using support vector machine
title_full_unstemmed Classification red blood cells using support vector machine
title_sort classification red blood cells using support vector machine
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2023
_version_ 1806428356423450624
score 13.214268

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