Impact of Chest X-ray Images Enhancement to COVID-19 Classification Using Vector Quantization and Fuzzy S-tree
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F22%3A10250400" target="_blank" >RIV/61989100:27240/22:10250400 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007/978-3-031-14627-5_38" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-14627-5_38</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-14627-5_38" target="_blank" >10.1007/978-3-031-14627-5_38</a>
Alternative languages
Result language
angličtina
Original language name
Impact of Chest X-ray Images Enhancement to COVID-19 Classification Using Vector Quantization and Fuzzy S-tree
Original language description
The chest X-ray (CXR) is a cheap and accurate method for lung-related disease diagnosis. The paper focuses on the COVID-19 detection in CXR images using the fuzzy medical image retrieval (FMIR) approach. Medical images often suffer from unbalanced brightness and low contrast, which reduce their readability. Therefore, an enhancement method is applied here to obtain a wider dynamic range of intensities and improve the visibility of details. The experiments test various parameters settings of the FMIR and compare the classification performance of the FMIR applied to original and enhanced images. The results show that the enhancement slightly improves the sensitivity, specificity and accuracy of the proposed method. (C) 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10200 - Computer and information sciences
Result continuities
Project
<a href="/en/project/GF22-34873K" target="_blank" >GF22-34873K: Constrained Multiobjective Optimization Based on Problem Landscape Analysis</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Lecture Notes in Networks and Systems. Volume 527
ISBN
978-3-031-14626-8
ISSN
2367-3370
e-ISSN
2367-3389
Number of pages
9
Pages from-to
371-379
Publisher name
Springer
Place of publication
Cham
Event location
Sanda
Event date
Sep 7, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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