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Automated Detection of Acute Lymphoblastic Leukemia From Microscopic Images Based on Human Visual Perception

Microscopic image analysis plays a significant role in initial leukemia screening and its efficient diagnostics. Since the present conventional methodologies partly rely on manual examination, which is time consuming and depends greatly on the experience of domain experts, automated leukemia detecti...

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Autores principales: Bodzas, Alexandra, Kodytek, Pavel, Zidek, Jan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7484487/
https://www.ncbi.nlm.nih.gov/pubmed/32984283
http://dx.doi.org/10.3389/fbioe.2020.01005
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author Bodzas, Alexandra
Kodytek, Pavel
Zidek, Jan
author_facet Bodzas, Alexandra
Kodytek, Pavel
Zidek, Jan
author_sort Bodzas, Alexandra
collection PubMed
description Microscopic image analysis plays a significant role in initial leukemia screening and its efficient diagnostics. Since the present conventional methodologies partly rely on manual examination, which is time consuming and depends greatly on the experience of domain experts, automated leukemia detection opens up new possibilities to minimize human intervention and provide more accurate clinical information. This paper proposes a novel approach based on conventional digital image processing techniques and machine learning algorithms to automatically identify acute lymphoblastic leukemia from peripheral blood smear images. To overcome the greatest challenges in the segmentation phase, we implemented extensive pre-processing and introduced a three-phase filtration algorithm to achieve the best segmentation results. Moreover, sixteen robust features were extracted from the images in the way that hematological experts do, which significantly increased the capability of the classifiers to recognize leukemic cells in microscopic images. To perform the classification, we applied two traditional machine learning classifiers, the artificial neural network and the support vector machine. Both methods reached a specificity of 95.31%, and the sensitivity of the support vector machine and artificial neural network reached 98.25 and 100%, respectively.
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spelling pubmed-74844872020-09-25 Automated Detection of Acute Lymphoblastic Leukemia From Microscopic Images Based on Human Visual Perception Bodzas, Alexandra Kodytek, Pavel Zidek, Jan Front Bioeng Biotechnol Bioengineering and Biotechnology Microscopic image analysis plays a significant role in initial leukemia screening and its efficient diagnostics. Since the present conventional methodologies partly rely on manual examination, which is time consuming and depends greatly on the experience of domain experts, automated leukemia detection opens up new possibilities to minimize human intervention and provide more accurate clinical information. This paper proposes a novel approach based on conventional digital image processing techniques and machine learning algorithms to automatically identify acute lymphoblastic leukemia from peripheral blood smear images. To overcome the greatest challenges in the segmentation phase, we implemented extensive pre-processing and introduced a three-phase filtration algorithm to achieve the best segmentation results. Moreover, sixteen robust features were extracted from the images in the way that hematological experts do, which significantly increased the capability of the classifiers to recognize leukemic cells in microscopic images. To perform the classification, we applied two traditional machine learning classifiers, the artificial neural network and the support vector machine. Both methods reached a specificity of 95.31%, and the sensitivity of the support vector machine and artificial neural network reached 98.25 and 100%, respectively. Frontiers Media S.A. 2020-08-28 /pmc/articles/PMC7484487/ /pubmed/32984283 http://dx.doi.org/10.3389/fbioe.2020.01005 Text en Copyright © 2020 Bodzas, Kodytek and Zidek. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Bioengineering and Biotechnology
Bodzas, Alexandra
Kodytek, Pavel
Zidek, Jan
Automated Detection of Acute Lymphoblastic Leukemia From Microscopic Images Based on Human Visual Perception
title Automated Detection of Acute Lymphoblastic Leukemia From Microscopic Images Based on Human Visual Perception
title_full Automated Detection of Acute Lymphoblastic Leukemia From Microscopic Images Based on Human Visual Perception
title_fullStr Automated Detection of Acute Lymphoblastic Leukemia From Microscopic Images Based on Human Visual Perception
title_full_unstemmed Automated Detection of Acute Lymphoblastic Leukemia From Microscopic Images Based on Human Visual Perception
title_short Automated Detection of Acute Lymphoblastic Leukemia From Microscopic Images Based on Human Visual Perception
title_sort automated detection of acute lymphoblastic leukemia from microscopic images based on human visual perception
topic Bioengineering and Biotechnology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7484487/
https://www.ncbi.nlm.nih.gov/pubmed/32984283
http://dx.doi.org/10.3389/fbioe.2020.01005
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