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 learning


In the 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.

Publication DOI: https://doi.org/10.1016/j.future.2022.02.022
Divisions: College of Business and Social Sciences > Aston Business School
College of Business and Social Sciences > Aston Business School > Operations & Information Management
Additional Information: © 2022, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ Funding Information: This work was supported in part by 2018 Guangzhou Science and Technology Innovation and Development of Special Funds , via Grant no. EF003/FST-FSJ/2019/GSTIC , and code no. 201907010001, and also VC Research ( VCR 0000149 ).
Uncontrolled Keywords: Algorithm,Classification model training,Deep learning,Machine learning,Medical dataset,Multi-class classification,Parameter optimization,Radiological images recognition,Software,Hardware and Architecture,Computer Networks and Communications
Publication ISSN: 1872-7115
Full Text Link:
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2022-08-01
Published Online Date: 2022-03-17
Accepted Date: 2022-02-25
Authors: Li, Tengyue
Fong, Simon
Mohammed, Sabah
Fiaidhi, Jinan
Guan, Steven
Chang, Victor (ORCID Profile 0000-0002-8012-5852)

Export / Share Citation


Additional statistics for this record