White Blood Cell Classification: Convolutional Neural Network (CNN) and Vision Transformer (ViT) under Medical Microscope

Deep learning (DL) has made significant advances in computer vision with the advent of vision transformers (ViTs). Unlike convolutional neural networks (CNNs), ViTs use self-attention to extract both local and global features from image data, and then apply residual connections to feed these feature...

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Detalles Bibliográficos
Autores: Ali, Mohamad Abou, Dornaika, Fadi, Arganda-Carreras, Ignacio
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2023
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/344017
Acceso en línea:http://hdl.handle.net/10261/344017
Access Level:acceso abierto
Palabra clave:Convolutional neural network (CNN)
Vision transformer (ViT)
ImageNet models
transfer learning (TL)
Machine learning (ML)
Deep learning (DP)
White blood cell classification
Peripheral blood cell (PBC)
Blood cell count and detection (BCCD)
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spelling White Blood Cell Classification: Convolutional Neural Network (CNN) and Vision Transformer (ViT) under Medical MicroscopeAli, Mohamad AbouDornaika, FadiArganda-Carreras, IgnacioConvolutional neural network (CNN)Vision transformer (ViT)ImageNet modelstransfer learning (TL)Machine learning (ML)Deep learning (DP)White blood cell classificationPeripheral blood cell (PBC)Blood cell count and detection (BCCD)Deep learning (DL) has made significant advances in computer vision with the advent of vision transformers (ViTs). Unlike convolutional neural networks (CNNs), ViTs use self-attention to extract both local and global features from image data, and then apply residual connections to feed these features directly into a fully networked multilayer perceptron head. In hospitals, hematologists prepare peripheral blood smears (PBSs) and read them under a medical microscope to detect abnormalities in blood counts such as leukemia. However, this task is time-consuming and prone to human error. This study investigated the transfer learning process of the Google ViT and ImageNet CNNs to automate the reading of PBSs. The study used two online PBS datasets, PBC and BCCD, and transferred them into balanced datasets to investigate the influence of data amount and noise immunity on both neural networks. The PBC results showed that the Google ViT is an excellent DL neural solution for data scarcity. The BCCD results showed that the Google ViT is superior to ImageNet CNNs in dealing with unclean, noisy image data because it is able to extract both global and local features and use residual connections, despite the additional time and computational overhead.This work is supported by grant PID2021-126701OB-I00 funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe”, and by grant GIU19/027 funded by the University of the Basque Country UPV/EHU.Peer reviewedMultidisciplinary Digital Publishing InstituteMinisterio de Ciencia e Innovación (España)Agencia Estatal de Investigación (España)European CommissionUniversidad del País VascoConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202420242023info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10261/344017reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2021-126701OB-I00The underlying dataset has been published as supplementary material of the article in the publisher platform at https://doi.org/10.3390/a16110525https://doi.org/10.3390/a16110525Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3440172026-05-22T06:33:51Z
dc.title.none.fl_str_mv White Blood Cell Classification: Convolutional Neural Network (CNN) and Vision Transformer (ViT) under Medical Microscope
title White Blood Cell Classification: Convolutional Neural Network (CNN) and Vision Transformer (ViT) under Medical Microscope
spellingShingle White Blood Cell Classification: Convolutional Neural Network (CNN) and Vision Transformer (ViT) under Medical Microscope
Ali, Mohamad Abou
Convolutional neural network (CNN)
Vision transformer (ViT)
ImageNet models
transfer learning (TL)
Machine learning (ML)
Deep learning (DP)
White blood cell classification
Peripheral blood cell (PBC)
Blood cell count and detection (BCCD)
title_short White Blood Cell Classification: Convolutional Neural Network (CNN) and Vision Transformer (ViT) under Medical Microscope
title_full White Blood Cell Classification: Convolutional Neural Network (CNN) and Vision Transformer (ViT) under Medical Microscope
title_fullStr White Blood Cell Classification: Convolutional Neural Network (CNN) and Vision Transformer (ViT) under Medical Microscope
title_full_unstemmed White Blood Cell Classification: Convolutional Neural Network (CNN) and Vision Transformer (ViT) under Medical Microscope
title_sort White Blood Cell Classification: Convolutional Neural Network (CNN) and Vision Transformer (ViT) under Medical Microscope
dc.creator.none.fl_str_mv Ali, Mohamad Abou
Dornaika, Fadi
Arganda-Carreras, Ignacio
author Ali, Mohamad Abou
author_facet Ali, Mohamad Abou
Dornaika, Fadi
Arganda-Carreras, Ignacio
author_role author
author2 Dornaika, Fadi
Arganda-Carreras, Ignacio
author2_role author
author
dc.contributor.none.fl_str_mv Ministerio de Ciencia e Innovación (España)
Agencia Estatal de Investigación (España)
European Commission
Universidad del País Vasco
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Convolutional neural network (CNN)
Vision transformer (ViT)
ImageNet models
transfer learning (TL)
Machine learning (ML)
Deep learning (DP)
White blood cell classification
Peripheral blood cell (PBC)
Blood cell count and detection (BCCD)
topic Convolutional neural network (CNN)
Vision transformer (ViT)
ImageNet models
transfer learning (TL)
Machine learning (ML)
Deep learning (DP)
White blood cell classification
Peripheral blood cell (PBC)
Blood cell count and detection (BCCD)
description Deep learning (DL) has made significant advances in computer vision with the advent of vision transformers (ViTs). Unlike convolutional neural networks (CNNs), ViTs use self-attention to extract both local and global features from image data, and then apply residual connections to feed these features directly into a fully networked multilayer perceptron head. In hospitals, hematologists prepare peripheral blood smears (PBSs) and read them under a medical microscope to detect abnormalities in blood counts such as leukemia. However, this task is time-consuming and prone to human error. This study investigated the transfer learning process of the Google ViT and ImageNet CNNs to automate the reading of PBSs. The study used two online PBS datasets, PBC and BCCD, and transferred them into balanced datasets to investigate the influence of data amount and noise immunity on both neural networks. The PBC results showed that the Google ViT is an excellent DL neural solution for data scarcity. The BCCD results showed that the Google ViT is superior to ImageNet CNNs in dealing with unclean, noisy image data because it is able to extract both global and local features and use residual connections, despite the additional time and computational overhead.
publishDate 2023
dc.date.none.fl_str_mv 2023
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/344017
url http://hdl.handle.net/10261/344017
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2021-126701OB-I00
The underlying dataset has been published as supplementary material of the article in the publisher platform at https://doi.org/10.3390/a16110525
https://doi.org/10.3390/a16110525
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute
publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,812429

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