Detection of post-COVID-19-related pulmonary diseases in X-ray images using Vision Transformer-based neural network

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F24%3APU148849" target="_blank" >RIV/00216305:26220/24:PU148849 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S1746809423008133" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1746809423008133</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.bspc.2023.105380" target="_blank" >10.1016/j.bspc.2023.105380</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Detection of post-COVID-19-related pulmonary diseases in X-ray images using Vision Transformer-based neural network

Popis výsledku v původním jazyce

Objective: Computer methods related to the diagnosis of COVID-19 disease have progressed significantly in recent years. Chest X-ray analysis supported by artificial intelligence is one of the most important parts of the diagnosis. Unfortunately, there is no digital tool dedicated to post-acute pulmonary changes related to COVID-19 and modern diagnostic tools are needed. Methods: This paper introduces a novel neural network architecture for chest X-ray analysis, which consists of two parts. The first is an Inception architecture that captures global features, and the second is a combination of Inception modules and a Vision Transformer network to analyze the local features. Considering that several diseases can occur in X-ray images together, a specific loss function for multilabel classification was applied - asymmetric loss function (ASL), which we modified for our purpose. In contrast to other works, we focus only on the subgroup of 9 diseases from the chestX-ray14 dataset, which can appear as a consequence of COVID-19. Results: This work proves the effectiveness of the proposed neural network architecture combined with the asymmetric loss function on post-COVID-related diseases. The results were compared with several wellknown classification architectures, such as VGG19, DenseNet121, EfficientNetB4, InceptionV3 and ResNet101. According to the results, the proposed method outperforms the mentioned models with AUC - 0.819, accuracy - 0.736, sensitivity - 0.7683, and specificity - 0.7221. Significance: Our work is the first one, which focuses on the diagnosis of post-COVID-19 related pulmonary diseases from X-ray images that uses deep learning. The proposed neural network reaches better accuracy than existing well-known architectures.

Název v anglickém jazyce

Detection of post-COVID-19-related pulmonary diseases in X-ray images using Vision Transformer-based neural network

Popis výsledku anglicky

Objective: Computer methods related to the diagnosis of COVID-19 disease have progressed significantly in recent years. Chest X-ray analysis supported by artificial intelligence is one of the most important parts of the diagnosis. Unfortunately, there is no digital tool dedicated to post-acute pulmonary changes related to COVID-19 and modern diagnostic tools are needed. Methods: This paper introduces a novel neural network architecture for chest X-ray analysis, which consists of two parts. The first is an Inception architecture that captures global features, and the second is a combination of Inception modules and a Vision Transformer network to analyze the local features. Considering that several diseases can occur in X-ray images together, a specific loss function for multilabel classification was applied - asymmetric loss function (ASL), which we modified for our purpose. In contrast to other works, we focus only on the subgroup of 9 diseases from the chestX-ray14 dataset, which can appear as a consequence of COVID-19. Results: This work proves the effectiveness of the proposed neural network architecture combined with the asymmetric loss function on post-COVID-related diseases. The results were compared with several wellknown classification architectures, such as VGG19, DenseNet121, EfficientNetB4, InceptionV3 and ResNet101. According to the results, the proposed method outperforms the mentioned models with AUC - 0.819, accuracy - 0.736, sensitivity - 0.7683, and specificity - 0.7221. Significance: Our work is the first one, which focuses on the diagnosis of post-COVID-19 related pulmonary diseases from X-ray images that uses deep learning. The proposed neural network reaches better accuracy than existing well-known architectures.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20600 - Medical engineering

Projekt

<a href="/cs/project/VK01010107" target="_blank" >VK01010107: Aplikace umělé inteligence pro forenzní identifikaci stop zeminových fází</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

Biomedical Signal Processing and Control

ISSN

1746-8094

e-ISSN

1746-8108

Svazek periodika

87

Číslo periodika v rámci svazku

A

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

11

Strana od-do

1-11

Kód UT WoS článku

001069282100001

EID výsledku v databázi Scopus

2-s2.0-85169579965