An Efficient Multi-Level Convolutional Neural Network Approach for White Blood Cells Classification

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dc.contributor.authorCheuque, César
dc.contributor.authorQuerales, Marvin
dc.contributor.authorLeón|, Roberto|Salas, Rodrigo
dc.contributor.authorTorres, Torres
dc.date.accessioned2022-11-30T02:46:49Z
dc.date.available2022-11-30T02:46:49Z
dc.date.issued2022
dc.description.abstractThe evaluation of white blood cells is essential to assess the quality of the human immune system; however, the assessment of the blood smear depends on the pathologist’s expertise. Most machine learning tools make a one-level classification for white blood cell classification. This work presents a two-stage hybrid multi-level scheme that efficiently classifies four cell groups: lymphocytes and monocytes (mononuclear) and segmented neutrophils and eosinophils (polymorphonuclear). At the first level, a Faster R-CNN network is applied for the identification of the region of interest of white blood cells, together with the separation of mononuclear cells from polymorphonuclear cells. Once separated, two parallel convolutional neural networks with the MobileNet structure are used to recognize the subclasses in the second level. The results obtained using Monte Carlo cross-validation show that the proposed model has a performance metric of around 98.4% (accuracy, recall, precision, and F1-score). The proposed model represents a good alternative for computer-aided diagnosis (CAD) tools for supporting the pathologist in the clinical laboratory in assessing white blood cells from blood smear images.en_ES
dc.facultadFacultad de Medicinaen_ES
dc.file.nameQuerales_Effi2022.pdf
dc.identifier.citationCheuque, C.; Querales, M.; León, R.; Salas, R.; Torres, R. An Efficient Multi-Level Convolutional Neural Network Approach for White Blood Cells Classification. Diagnostics 2022, 12, 248. https://doi.org/10.3390/diagnostics12020248en_ES
dc.identifier.doihttps://doi.org/10.3390/diagnostics12020248
dc.identifier.urihttp://repositoriobibliotecas.uv.cl/handle/uvscl/7503
dc.languageen
dc.publisherMDPI
dc.rightsCopyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
dc.sourceDiagnostics
dc.subjectWHITE BLOOD CELLS CLASSIFICATIONen_ES
dc.subjectDEEP LEARNINGen_ES
dc.subjectMULTI-LEVEL CLASSIFICATIONen_ES
dc.subjectMULTISOURCE DATASETSen_ES
dc.titleAn Efficient Multi-Level Convolutional Neural Network Approach for White Blood Cells Classification
dc.typeArticulo
uv.departamentoEscuela de Tecnologia Medica

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