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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

<a href="/cs/project/EF16_019%2F0000867" target="_blank" >EF16_019/0000867: Centrum výzkumu pokročilých mechatronických systémů</a><br>

Návaznosti

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

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název periodika

Frontiers in Bioengineering and Biotechnology

ISSN

2296-4185

e-ISSN

—

Svazek periodika

8

Číslo periodika v rámci svazku

1005

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

13

Strana od-do

—

Kód UT WoS článku

000570421300001

EID výsledku v databázi Scopus

2-s2.0-85090778562