classification of dispersed patterns of radiographic images with covid 19 by core periphery CORD-Papers (Version 1)

Title: Classification of Dispersed Patterns of Radiographic Images with COVID-19 by Core-Periphery Network Modeling
Abstract: In real world data classification tasks we always face the situations where the data samples of the normal cases present a well defined pattern and the features of abnormal data samples vary from one to another i.e. do not show a regular pattern. Up to now the general data classification hypothesis requires the data features within each class to present a certain level of similarity. Therefore such real situations violate the classic classification condition and make it a hard task. In this paper we present a novel solution for this kind of problems through a network approach. Specifically we construct a core-periphery network from the training data set in such way that core node set is formed by the normal data samples and peripheral node set contains the abnormal samples of the training data set. The classification is made by checking the coreness of the testing data samples. The proposed method is applied to classify radiographic image for COVID-19 diagnosis. Computer simulations show promising results of the method. The main contribution is to introduce a general scheme to characterize pattern formation of the data without pattern. 2022 The Author(s) under exclusive license to Springer Nature Switzerland AG.
Published: 2022
Journal: 10th International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2021
DOI: 10.1007/978-3-030-93409-5_4
Author Name: Yan J
Author link:
Author Name: Liu W
Author link:
Author Name: Zhu Y T
Author link:
Author Name: Li G
Author link:
Author Name: Zheng Q
Author link:
Author Name: Zhao L
Author link:
license: unk
license_url: [unknown license]
source_x: WHO
who_covidence_id: #covidwho-1626567
has_full_text: FALSE
G_ID: classification_of_dispersed_patterns_of_radiographic_images_with_covid_19_by_core_periphery