Chest X-ray Image Analysis using Convolutional Vision Transformer

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F23%3APU148419" target="_blank" >RIV/00216305:26220/23:PU148419 - isvavai.cz</a>
Result on the web
<a href="https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2023_sbornik_2_v2.pdf" target="_blank" >https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2023_sbornik_2_v2.pdf</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.13164/eeict.2023.161" target="_blank" >10.13164/eeict.2023.161</a>

Result language
angličtina
Original language name
Chest X-ray Image Analysis using Convolutional Vision Transformer
Original language description
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.
Czech name
—
Czech description
—

Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings II of the 29th Conference STUDENT EEICT 2023 Selected papers
ISBN
978-80-214-6154-3
ISSN
2788-1334
e-ISSN
—
Number of pages
5
Pages from-to
161-165
Publisher name
Brno University of Technology, Faculty of Electrical Engineering and Communication
Place of publication
Brno
Event location
Brno
Event date
Apr 25, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
—

Similar results(10)