Original title: Chest X-ray Image Analysis using Convolutional Vision Transformer
Authors: Mezina, Anzhelika ; Burget, Radim
Document type: Papers
Language: eng
Publisher: Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií
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: chest Xrayimages; deep learning; InceptionNetV3; multilabel classification; Vision transformer
Host item entry: Proceedings II of the 29st Conference STUDENT EEICT 2023: Selected papers, ISBN 978-80-214-6154-3, ISSN 2788-1334

Institution: Brno University of Technology (web)
Document availability information: Fulltext is available in the Brno University of Technology Digital Library.
Original record: http://hdl.handle.net/11012/210681

Permalink: http://www.nusl.cz/ntk/nusl-531787


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