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Empowering multi-class medical data classification by Group-of-Single-Class-predictors and transfer optimization: Cases of structured dataset by machine learning and radiological images by deep learni
Li, Tengyue1; Fong, Simon1; Mohammed, Sabah2; Fiaidhi, Jinan2; Guan, Steven3; Chang, Victor4
2022-08
Source PublicationFuture Generation Computer Systems
ISSN0167-739X
Volume133Issue:1Pages:10-22
Abstract

In medical domain, data are often collected over time, evolving from simple to refined categories. The data and the underlying structures of the medical data as to how they have grown to today’s complexity can be decomposed into crude forms when data collection starts. For instance, the cancer dataset is labeled either benign or malignant at its simplest or perhaps the earliest form. As medical knowledge advances and/or more data become available, the dataset progresses from binary class to multi-class, having more labels of sub-categories of the disease added. In machine learning, inducing a multi-class model requires more computational power. Model optimization is enforced over the multi-class models for the highest possible accuracy, which of course, is necessary for life-and-death decision making. This model optimization task consumes an extremely long model training time. In this paper, a novel strategy called Group-of-Single-Class prediction (GOSC) coupled with majority voting and model transfer is proposed for achieving maximum accuracy by using only a fraction of the model training time. The main advantage is the ability to achieve an optimized multi-class classification model that has the highest possible accuracy near to the absolute maximum, while the training time could be saved by up to 70%. Experiments on machine learning over liver dataset classification and deep learning over COVID19 lung CT images were tested. Preliminary results suggest the feasibility of this new approach.

KeywordMachine Learning Deep Learning Multi-class Classification Parameter Optimization Classification Model Training Medical Dataset Radiological Images Recognition
DOI10.1016/j.future.2022.02.022
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000806791200002
PublisherElsevier
Scopus ID2-s2.0-85126595008
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Citation statistics
Cited Times [WOS]:1   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Corresponding AuthorFong, Simon; Chang, Victor
Affiliation1.University of Macau, Macau SAR
2.Lakehead University, Canada
3.Xl’an Jiaotoing-Liverpool University, Suzhou, China
4.Aston University, Birmingham, UK
First Author AffilicationUniversity of Macau
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Li, Tengyue,Fong, Simon,Mohammed, Sabah,et al. Empowering multi-class medical data classification by Group-of-Single-Class-predictors and transfer optimization: Cases of structured dataset by machine learning and radiological images by deep learni[J]. Future Generation Computer Systems,2022,133(1):10-22.
APA Li, Tengyue,Fong, Simon,Mohammed, Sabah,Fiaidhi, Jinan,Guan, Steven,&Chang, Victor.(2022).Empowering multi-class medical data classification by Group-of-Single-Class-predictors and transfer optimization: Cases of structured dataset by machine learning and radiological images by deep learni.Future Generation Computer Systems,133(1),10-22.
MLA Li, Tengyue,et al."Empowering multi-class medical data classification by Group-of-Single-Class-predictors and transfer optimization: Cases of structured dataset by machine learning and radiological images by deep learni".Future Generation Computer Systems 133.1(2022):10-22.
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