Publication detail

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

MEZINA, A. BURGET, R.

Original Title

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

Type

journal article in Web of Science

Language

English

Original Abstract

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.

Keywords

Image classification; Deep learning; Chest X-ray images; InceptionV3; Vision transformer

Authors

MEZINA, A.; BURGET, R.

Released

3. 1. 2024

Publisher

ELSEVIER SCI LTD

Location

OXFORD

ISBN

1746-8108

Periodical

Biomedical Signal Processing and Control

Year of study

87

Number

A

State

United Kingdom of Great Britain and Northern Ireland

Pages from

1

Pages to

11

Pages count

11

URL

https://www.sciencedirect.com/science/article/pii/S1746809423008133

BibTex

@article{BUT184431,
  author="Anzhelika {Mezina} and Radim {Burget}",
  title="Detection of post-COVID-19-related pulmonary diseases in X-ray images using Vision Transformer-based neural network",
  journal="Biomedical Signal Processing and Control",
  year="2024",
  volume="87",
  number="A",
  pages="1--11",
  doi="10.1016/j.bspc.2023.105380",
  issn="1746-8108",
  url="https://www.sciencedirect.com/science/article/pii/S1746809423008133"
}