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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F20%3A10245470" target="_blank" >RIV/61989100:27240/20:10245470 - isvavai.cz</a>

Result on the web

<a href="https://www.frontiersin.org/articles/10.3389/fbioe.2020.01005/full" target="_blank" >https://www.frontiersin.org/articles/10.3389/fbioe.2020.01005/full</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3389/fbioe.2020.01005" target="_blank" >10.3389/fbioe.2020.01005</a>

Result language

angličtina

Original language name

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

Original language 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. (C) Copyright (C) 2020 Bodzas, Kodytek and Zidek.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

20201 - Electrical and electronic engineering

Project

<a href="/en/project/EF16_019%2F0000867" target="_blank" >EF16_019/0000867: Research Centre of Advanced Mechatronic Systems</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year

2020

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Frontiers in Bioengineering and Biotechnology

ISSN

2296-4185

e-ISSN

—

Volume of the periodical

8

Issue of the periodical within the volume

1005

Country of publishing house

CH - SWITZERLAND

Number of pages

13

Pages from-to

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UT code for WoS article

000570421300001

EID of the result in the Scopus database

2-s2.0-85090778562