Multi-task multi-modality SVM for early COVID-19 Diagnosis using chest CT data.

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dc.citation.issue1
dc.citation.volume59
dc.contributor.authorHu R
dc.contributor.authorGan J
dc.contributor.authorZhu X
dc.contributor.authorLiu T
dc.contributor.authorShi X
dc.date.accessioned2023-11-20T01:37:40Z
dc.date.available2022-01
dc.date.available2021-09-23
dc.date.available2023-11-20T01:37:40Z
dc.date.issued2022-01
dc.descriptionSince January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID 19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active
dc.description.abstractIn the early diagnosis of the Coronavirus disease (COVID-19), it is of great importance for either distinguishing severe cases from mild cases or predicting the conversion time that mild cases would possibly convert to severe cases. This study investigates both of them in a unified framework by exploring the problems such as slight appearance difference between mild cases and severe cases, the interpretability, the High Dimension and Low Sample Size (HDLSS) data, and the class imbalance. To this end, the proposed framework includes three steps: (1) feature extraction which first conducts the hierarchical segmentation on the chest Computed Tomography (CT) image data and then extracts multi-modality handcrafted features for each segment, aiming at capturing the slight appearance difference from different perspectives; (2) data augmentation which employs the over-sampling technique to augment the number of samples corresponding to the minority classes, aiming at investigating the class imbalance problem; and (3) joint construction of classification and regression by proposing a novel Multi-task Multi-modality Support Vector Machine (MM-SVM) method to solve the issue of the HDLSS data and achieve the interpretability. Experimental analysis on two synthetic and one real COVID-19 data set demonstrated that our proposed framework outperformed six state-of-the-art methods in terms of binary classification and regression performance.
dc.description.publication-statusPublished
dc.identifierhttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000711839100004&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=c5bb3b2499afac691c2e3c1a83ef6fef
dc.identifierARTN 102782
dc.identifier.citationINFORMATION PROCESSING & MANAGEMENT, 2022, 59 (1)
dc.identifier.doi10.1016/j.ipm.2021.102782
dc.identifier.eissn1873-5371
dc.identifier.elements-id449111
dc.identifier.harvestedMassey_Dark
dc.identifier.issn0306-4573
dc.identifier.urihttps://hdl.handle.net/10179/17424
dc.publisherElsevier Ltd
dc.relation.isPartOfINFORMATION PROCESSING & MANAGEMENT
dc.subjectCOVID-19
dc.subjectChest CT scans
dc.subjectSVM
dc.subjectMulti-task learning
dc.subjectMulti-modality fusion
dc.subject.anzsrc0804 Data Format
dc.subject.anzsrc0806 Information Systems
dc.subject.anzsrc0807 Library and Information Studies
dc.titleMulti-task multi-modality SVM for early COVID-19 Diagnosis using chest CT data.
dc.typeJournal article
pubs.notesNot known
pubs.organisational-group/Massey University
pubs.organisational-group/Massey University/College of Sciences
pubs.organisational-group/Massey University/College of Sciences/School of Mathematical and Computational Sciences
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