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

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

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

Original language description

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.

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

—

OECD FORD branch

20600 - Medical engineering

Project

<a href="/en/project/VK01010107" target="_blank" >VK01010107: Application of artificial intelligence for forensic identification of soil phases</a><br>

Continuities

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

Publication year

2024

Confidentiality

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

Name of the periodical

Biomedical Signal Processing and Control

ISSN

1746-8094

e-ISSN

1746-8108

Volume of the periodical

87

Issue of the periodical within the volume

A

Country of publishing house

GB - UNITED KINGDOM

Number of pages

11

Pages from-to

1-11

UT code for WoS article

001069282100001

EID of the result in the Scopus database

2-s2.0-85169579965