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IJMLC 2021 Vol.11(6): 387-392 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2021.11.6.1066

Progressive Breast Cancer Diagnosis Model Based on Multi-classifier and Multi-modal Fusion

Jiyun Li, Chenxi Jia, and Chen Qian

Abstract—The clinical diagnosis of breast cancer in real life is a comprehensive process which needs to consider different sources of information and use different medical examination methods according to different stages of the disease. First, routine and more economical medical examination should be carried out according to the needs of the disease, and then more accurate but expensive examination should be carried out according to the condition. When the data is seriously missing while the required features are selected, it will seriously affect the accuracy of the traditional comprehensive diagnosis model. A large amount of data is missing due to partial inspections that have not been performed within a certain period of time. At this time, the accuracy of traditional model will be greatly reduced. In order to solve this problem, this paper proposes a progressive breast cancer diagnosis strategy using multi-criteria and multi-classifier fusion that realizes the development according to the course of disease and continuously supplements the examination information to achieve a progressive comprehensive diagnosis of breast cancer. The architecture also has good scalability, which can be extended to more types of classifiers and input information of different modes, so as to achieve multi-criteria and multi-source comprehensive decision. Compared with the traditional multi-source breast cancer comprehensive diagnosis strategy, the experimental results show that the progressive breast cancer comprehensive diagnosis strategy has better predictive performance and clinical practicability.

Index Terms—Breast cancer, multi-classifier fusion, multi-modal fusion, progressive diagnosis.

The authors are with the College of Computer Science and Technology, Donghua University, Shanghai, China (e-mail: jyli@dhu.edu.cn, 2181829@mail.dhu.edu.cn, chen.qian@dhu.edu.cn).


Cite: Jiyun Li, Chenxi Jia, and Chen Qian, "Progressive Breast Cancer Diagnosis Model Based on Multi-classifier and Multi-modal Fusion," International Journal of Machine Learning and Computing vol. 11, no. 6, pp. 387-392, 2021.

Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Quaterly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net

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