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Classification of White Blood Cells: A Comprehensive Study Using Transfer Learning Based on Convolutional Neural Networks

White blood cells (WBCs) in the human immune system defend against infection and protect the body from external hazardous objects. They are comprised of neutrophils, eosinophils, basophils, monocytes, and lymphocytes, whereby each accounts for a distinct percentage and performs specific functions. T...

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Detalles Bibliográficos
Autores principales: Tamang, Thinam, Baral, Sushish, Paing, May Phu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9777002/
https://www.ncbi.nlm.nih.gov/pubmed/36552910
http://dx.doi.org/10.3390/diagnostics12122903
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author Tamang, Thinam
Baral, Sushish
Paing, May Phu
author_facet Tamang, Thinam
Baral, Sushish
Paing, May Phu
author_sort Tamang, Thinam
collection PubMed
description White blood cells (WBCs) in the human immune system defend against infection and protect the body from external hazardous objects. They are comprised of neutrophils, eosinophils, basophils, monocytes, and lymphocytes, whereby each accounts for a distinct percentage and performs specific functions. Traditionally, the clinical laboratory procedure for quantifying the specific types of white blood cells is an integral part of a complete blood count (CBC) test, which aids in monitoring the health of people. With the advancements in deep learning, blood film images can be classified in less time and with high accuracy using various algorithms. This paper exploits a number of state-of-the-art deep learning models and their variations based on CNN architecture. A comparative study on model performance based on accuracy, F1-score, recall, precision, number of parameters, and time was conducted, and DenseNet161 was found to demonstrate a superior performance among its counterparts. In addition, advanced optimization techniques such as normalization, mixed-up augmentation, and label smoothing were also employed on DenseNet to further refine its performance.
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spelling pubmed-97770022022-12-23 Classification of White Blood Cells: A Comprehensive Study Using Transfer Learning Based on Convolutional Neural Networks Tamang, Thinam Baral, Sushish Paing, May Phu Diagnostics (Basel) Article White blood cells (WBCs) in the human immune system defend against infection and protect the body from external hazardous objects. They are comprised of neutrophils, eosinophils, basophils, monocytes, and lymphocytes, whereby each accounts for a distinct percentage and performs specific functions. Traditionally, the clinical laboratory procedure for quantifying the specific types of white blood cells is an integral part of a complete blood count (CBC) test, which aids in monitoring the health of people. With the advancements in deep learning, blood film images can be classified in less time and with high accuracy using various algorithms. This paper exploits a number of state-of-the-art deep learning models and their variations based on CNN architecture. A comparative study on model performance based on accuracy, F1-score, recall, precision, number of parameters, and time was conducted, and DenseNet161 was found to demonstrate a superior performance among its counterparts. In addition, advanced optimization techniques such as normalization, mixed-up augmentation, and label smoothing were also employed on DenseNet to further refine its performance. MDPI 2022-11-22 /pmc/articles/PMC9777002/ /pubmed/36552910 http://dx.doi.org/10.3390/diagnostics12122903 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/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/).
spellingShingle Article
Tamang, Thinam
Baral, Sushish
Paing, May Phu
Classification of White Blood Cells: A Comprehensive Study Using Transfer Learning Based on Convolutional Neural Networks
title Classification of White Blood Cells: A Comprehensive Study Using Transfer Learning Based on Convolutional Neural Networks
title_full Classification of White Blood Cells: A Comprehensive Study Using Transfer Learning Based on Convolutional Neural Networks
title_fullStr Classification of White Blood Cells: A Comprehensive Study Using Transfer Learning Based on Convolutional Neural Networks
title_full_unstemmed Classification of White Blood Cells: A Comprehensive Study Using Transfer Learning Based on Convolutional Neural Networks
title_short Classification of White Blood Cells: A Comprehensive Study Using Transfer Learning Based on Convolutional Neural Networks
title_sort classification of white blood cells: a comprehensive study using transfer learning based on convolutional neural networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9777002/
https://www.ncbi.nlm.nih.gov/pubmed/36552910
http://dx.doi.org/10.3390/diagnostics12122903
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