Publication detail
Chest X-ray Image Analysis using Convolutional Vision Transformer
MEZINA, A. BURGET, R.
Original Title
Chest X-ray Image Analysis using Convolutional Vision Transformer
Type
conference paper
Language
English
Original Abstract
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.
Keywords
deep learning, multilabel classification, chest Xray images, Vision transformer, InceptionNetV3
Authors
MEZINA, A.; BURGET, R.
Released
25. 4. 2023
Publisher
Brno University of Technology, Faculty of Electrical Engineering and Communication
Location
Brno
ISBN
978-80-214-6154-3
Book
Proceedings II of the 29th Conference STUDENT EEICT 2023 Selected papers
Edition
1
ISBN
2788-1334
Periodical
Proceedings II of the Conference STUDENT EEICT
State
Czech Republic
Pages from
161
Pages to
165
Pages count
5
URL
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"
}