Chest X-ray Image Analysis using Convolutional Vision Transformer

Mezina, Anzhelika; Burget, Radim

Chest X-ray Image Analysis using Convolutional Vision Transformer

Files

161_EEICT_selected.pdf(794.46 KB)

Date

2023

Authors

Mezina, Anzhelika

Burget, Radim

Publisher

Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií

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Abstract

In recent years, computer techniques for clinical imageanalysis have been improved significantly, especially becauseof the pandemic situation. Most recent approaches are focusedon 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 theneural network, which is aimed to detect 14 pulmonary diseases.This model is composed of two branches: global, which is theInceptionNetV3, and local, which consists of Inception modulesand a modified Vision Transformer. Additionally, the AsymmetricLoss function was utilized to deal with the problem of multilabelclassification. The proposed model has achieved an AUC of 0.8012and an accuracy of 0.7429, which outperforms the well-knownclassification models.

Keywords

deep learning, multilabel classification, chest Xrayimages, Vision transformer, InceptionNetV3

Citation

Proceedings II of the 29st Conference STUDENT EEICT 2023: Selected papers. s. 161-165. ISBN 978-80-214-6154-3
https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2023_sbornik_2_v2.pdf

Document type

Peer-reviewed

Document version

Published version

Language of document

en

Document licence