Detail publikace

Chest X-ray Image Analysis using Convolutional Vision Transformer

MEZINA, A. BURGET, R.

Originální název

Chest X-ray Image Analysis using Convolutional Vision Transformer

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

In recent years, computer techniques for clinical image analysis have been improved significantly, especially because of the pandemic situation. Most recent approaches are focused on the detection of viral pneumonia or COVID-19 diseases. However, there is less attention to common pulmonary diseases, such as fibrosis, infiltration and others. This paper introduces the neural network, which is aimed to detect 14 pulmonary diseases. This model is composed of two branches: global, which is the InceptionNetV3, and local, which consists of Inception modules and a modified Vision Transformer. Additionally, the Asymmetric Loss function was utilized to deal with the problem of multilabel classification. The proposed model has achieved an AUC of 0.8012 and an accuracy of 0.7429, which outperforms the well-known classification models.

Klíčová slova

deep learning, multilabel classification, chest Xray images, Vision transformer, InceptionNetV3

Autoři

MEZINA, A.; BURGET, R.

Vydáno

25. 4. 2023

Nakladatel

Brno University of Technology, Faculty of Electrical Engineering and Communication

Místo

Brno

ISBN

978-80-214-6154-3

Kniha

Proceedings II of the 29th Conference STUDENT EEICT 2023 Selected papers

Edice

1

ISSN

2788-1334

Periodikum

Proceedings II of the Conference STUDENT EEICT

Stát

Česká republika

Strany od

161

Strany do

165

Strany počet

5

URL

https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2023_sbornik_2_v2.pdf

BibTex

@inproceedings{BUT183898,
  author="Anzhelika {Mezina} and Radim {Burget}",
  title="Chest X-ray Image Analysis using Convolutional Vision Transformer",
  booktitle="Proceedings II of the 29th Conference STUDENT EEICT 2023 Selected papers",
  year="2023",
  series="1",
  journal="Proceedings II of the Conference STUDENT EEICT",
  pages="161--165",
  publisher="Brno University of Technology, Faculty of Electrical Engineering and Communication",
  address="Brno",
  doi="10.13164/eeict.2023.161",
  isbn="978-80-214-6154-3",
  issn="2788-1334",
  url="https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2023_sbornik_2_v2.pdf"
}