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Classification red blood cells using support vector machine

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dc.citedby13
dc.contributor.authorAkrimi J.A.en_US
dc.contributor.authorSuliman A.en_US
dc.contributor.authorGeorge L.E.en_US
dc.contributor.authorAhmad A.R.en_US
dc.contributor.authorid56728894600en_US
dc.contributor.authorid25825739000en_US
dc.contributor.authorid56038298400en_US
dc.contributor.authorid35589598800en_US
dc.date.accessioned2023-05-29T06:00:40Z
dc.date.available2023-05-29T06:00:40Z
dc.date.issued2015
dc.descriptionCells; 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; Blooden_US
dc.description.abstractThe 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.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo7066642
dc.identifier.doi10.1109/ICIMU.2014.7066642
dc.identifier.epage269
dc.identifier.scopus2-s2.0-84937468175
dc.identifier.spage265
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84937468175&doi=10.1109%2fICIMU.2014.7066642&partnerID=40&md5=d126f88afcdd806000a29eb33125df5f
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/22387
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitleConference Proceedings - 6th International Conference on Information Technology and Multimedia at UNITEN: Cultivating Creativity and Enabling Technology Through the Internet of Things, ICIMU 2014
dc.titleClassification red blood cells using support vector machineen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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