A Vision Transformer Based Approach for Analysis of Plasmodium Vivax Life Cycle for Malaria Prediction Using Thin Blood Smear Microscopic Images
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F22%3APU144944" target="_blank" >RIV/00216305:26220/22:PU144944 - isvavai.cz</a>
Výsledek na webu
<a href="https://www.sciencedirect.com/science/article/abs/pii/S0169260722003789" target="_blank" >https://www.sciencedirect.com/science/article/abs/pii/S0169260722003789</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.cmpb.2022.106996" target="_blank" >10.1016/j.cmpb.2022.106996</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
A Vision Transformer Based Approach for Analysis of Plasmodium Vivax Life Cycle for Malaria Prediction Using Thin Blood Smear Microscopic Images
Popis výsledku v původním jazyce
Background and objectives Microscopic images are an important part for haematologists in diagnosing various diseases in the blood cell. Changes in blood cells are caused by malaria disease, and early diagnosis can prevent the disease from entering its severe stage. Methods In this paper, an automated non-invasive and efficient deep learning-based framework is developed for multi-class plasmodium vivax life cycle classification and malaria diagnosis. A multi-class microscopic blood cell of different plasmodium vivax life cycle stage dataset is analysed, and a diagnostic framework is designed. Several stages of the disease are examined and augmented through various techniques to make the framework robust in real-time. Generative adversarial network is specially designed to generate extended training samples of various life cycle stages to increase robustness of the resulting model. A special transformer-based neural network vision transformer is designed to improve generalisation capabilities. Microscopic images are classified into multi classes of plasmodium vivax life cycle stage, where the keystone transformer layers extract relevant disease features from microscopic colour images, and the extracted relevant features are used to make predictive diagnostic decisions. Results The capabilities of the vision transformer are computed and analysed by statistical parameters, and the performance of the vision transformer model is compared with baseline architectures, where it was evident that the performance of the vision transformer was significantly better, reaching 90.03% accuracy. Conclusions A comprehensive comparison of the proposed framework to the state-of-the-art methods proves its efficiency in the classification of plasmodium vivax life cycle for malaria disease identification through thin blood smear microscopic images.Background and objectives Microscopic images are an important part for haematologists in diagnosing various diseases in the blood cell. Chang
Název v anglickém jazyce
A Vision Transformer Based Approach for Analysis of Plasmodium Vivax Life Cycle for Malaria Prediction Using Thin Blood Smear Microscopic Images
Popis výsledku anglicky
Background and objectives Microscopic images are an important part for haematologists in diagnosing various diseases in the blood cell. Changes in blood cells are caused by malaria disease, and early diagnosis can prevent the disease from entering its severe stage. Methods In this paper, an automated non-invasive and efficient deep learning-based framework is developed for multi-class plasmodium vivax life cycle classification and malaria diagnosis. A multi-class microscopic blood cell of different plasmodium vivax life cycle stage dataset is analysed, and a diagnostic framework is designed. Several stages of the disease are examined and augmented through various techniques to make the framework robust in real-time. Generative adversarial network is specially designed to generate extended training samples of various life cycle stages to increase robustness of the resulting model. A special transformer-based neural network vision transformer is designed to improve generalisation capabilities. Microscopic images are classified into multi classes of plasmodium vivax life cycle stage, where the keystone transformer layers extract relevant disease features from microscopic colour images, and the extracted relevant features are used to make predictive diagnostic decisions. Results The capabilities of the vision transformer are computed and analysed by statistical parameters, and the performance of the vision transformer model is compared with baseline architectures, where it was evident that the performance of the vision transformer was significantly better, reaching 90.03% accuracy. Conclusions A comprehensive comparison of the proposed framework to the state-of-the-art methods proves its efficiency in the classification of plasmodium vivax life cycle for malaria disease identification through thin blood smear microscopic images.Background and objectives Microscopic images are an important part for haematologists in diagnosing various diseases in the blood cell. Chang
Klasifikace
Druh
J<sub>imp</sub> - Článek v periodiku v databázi Web of Science
CEP obor
—
OECD FORD obor
20202 - Communication engineering and systems
Návaznosti výsledku
Projekt
—
Návaznosti
S - Specificky vyzkum na vysokych skolach
Ostatní
Rok uplatnění
2022
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název periodika
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
ISSN
0169-2607
e-ISSN
1872-7565
Svazek periodika
224
Číslo periodika v rámci svazku
1
Stát vydavatele periodika
NL - Nizozemsko
Počet stran výsledku
13
Strana od-do
1-13
Kód UT WoS článku
000839021700010
EID výsledku v databázi Scopus
2-s2.0-85134337086