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dc.contributor.authorRustam, Furqan
dc.contributor.authorAslam, Naila
dc.contributor.authorTorre Díez, Isabel de la 
dc.contributor.authorKhan, Yaser Daanial
dc.contributor.authorVidal Mazón, Juan Luis
dc.contributor.authorRodríguez, Carmen Lili
dc.contributor.authorAshraf, Imran
dc.date.accessioned2023-07-04T11:15:33Z
dc.date.available2023-07-04T11:15:33Z
dc.date.issued2022
dc.identifier.citationHealthcare, 2022, Vol. 10, Nº. 11, 2230es
dc.identifier.issn2227-9032es
dc.identifier.urihttps://uvadoc.uva.es/handle/10324/60054
dc.descriptionProducción Científicaes
dc.description.abstractWhite blood cell (WBC) type classification is a task of significant importance for diagnosis using microscopic images of WBC, which develop immunity to fight against infections and foreign substances. WBCs consist of different types, and abnormalities in a type of WBC may potentially represent a disease such as leukemia. Existing studies are limited by low accuracy and overrated performance, often caused by model overfit due to an imbalanced dataset. Additionally, many studies consider a lower number of WBC types, and the accuracy is exaggerated. This study presents a hybrid feature set of selective features and synthetic minority oversampling technique-based resampling to mitigate the influence of the above-mentioned problems. Furthermore, machine learning models are adopted for being less computationally complex, requiring less data for training, and providing robust results. Experiments are performed using both machine- and deep learning models for performance comparison using the original dataset, augmented dataset, and oversampled dataset to analyze the performances of the models. The results suggest that a hybrid feature set of both texture and RGB features from microscopic images, selected using Chi2, produces a high accuracy of 0.97 with random forest. Performance appraisal using k-fold cross-validation and comparison with existing state-of-the-art studies shows that the proposed approach outperforms existing studies regarding the obtained accuracy and computational complexity.es
dc.format.mimetypeapplication/pdfes
dc.language.isoenges
dc.publisherMDPIes
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectLeucocyteses
dc.subjectHematologyes
dc.subjectSangre - Glóbulos blancoses
dc.subjectBlood - Diseaseses
dc.subjectSangre - Enfermedadeses
dc.subjectLeukemiaes
dc.subjectLeucemiaes
dc.subjectImage processinges
dc.subjectDiagnóstico por imágenes - Técnicas digitaleses
dc.subjectImágenes, Sistemas de, en medicinaes
dc.subjectComputational intelligencees
dc.subjectMicroscopyes
dc.subjectMicroscopia médicaes
dc.titleWhite blood cell classification using texture and RGB features of oversampled microscopic imageses
dc.typeinfo:eu-repo/semantics/articlees
dc.rights.holder© 2022 The Authorses
dc.identifier.doi10.3390/healthcare10112230es
dc.relation.publisherversionhttps://www.mdpi.com/2227-9032/10/11/2230es
dc.identifier.publicationfirstpage2230es
dc.identifier.publicationissue11es
dc.identifier.publicationtitleHealthcarees
dc.identifier.publicationvolume10es
dc.peerreviewedSIes
dc.identifier.essn2227-9032es
dc.rightsAtribución 4.0 Internacional*
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones
dc.subject.unesco3205.04 Hematologíaes
dc.subject.unesco3207.13 Oncologíaes
dc.subject.unesco2209.90 Tratamiento Digital. Imágeneses


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