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sis 22(2): e4

Research Article

ODET: Optimized Deep ELM-based Transfer Learning for Breast Cancer Explainable Detection

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  • @ARTICLE{10.4108/eetsis.v9i6.1747,
        author={Ziquan Zhu and Shuihua Wang},
        title={ODET: Optimized Deep ELM-based Transfer Learning for Breast Cancer Explainable Detection},
        journal={EAI Endorsed Transactions on Scalable Information Systems},
        volume={10},
        number={2},
        publisher={EAI},
        journal_a={SIS},
        year={2022},
        month={9},
        keywords={breast cancer, ResNet50, bat algorithm, Extreme learning Machine, ultrasound image},
        doi={10.4108/eetsis.v9i6.1747}
    }
    
  • Ziquan Zhu
    Shuihua Wang
    Year: 2022
    ODET: Optimized Deep ELM-based Transfer Learning for Breast Cancer Explainable Detection
    SIS
    EAI
    DOI: 10.4108/eetsis.v9i6.1747
Ziquan Zhu1, Shuihua Wang2,*
  • 1: University of Florida
  • 2: Henan Polytechnic University
*Contact email: shuihuawang@ieee.org

Abstract

INTRODUCTION: Breast cancer is one of the most common malignant tumors in women, and the incidence rate is increasing year by year. Women in every country in the world may develop breast cancer at any age after puberty. The cause of breast cancer is not fully understood. At present, the main methods of breast cancer detection are inefficient. Researchers are trying to use computer technology to detect breast cancer. But there are some still limitations. METHODS: We propose a network (ODET) to detect breast cancer based on ultrasound images. In this paper, we use ResNet50 as the backbone model. We make some modifications to the backbone model by deep ELM-based transfer learning. After these modifications, the network is named DET. However, DET still has some shortcomings because the parameters in DET are randomly assigned and will not change in the experiment. In this case, we select BA to optimize DET. The optimized DET is named ODET. RESULTS: The proposed ODET gets the F1-score (F1), precision (PRE), specificity (SPE), sensitivity (SEN), and accuracy (ACC) are 93.16%±1.12%, 93.28%±1.36%, 98.63%±0.31%, 93.96%±1.85%, and 97.84%±0.37%, respectively. CONCLUSION: It proves that the proposed ODET is an effective method for breast cancer detection.

Keywords
breast cancer, ResNet50, bat algorithm, Extreme learning Machine, ultrasound image
Received
2022-06-30
Accepted
2022-09-05
Published
2022-09-29
Publisher
EAI
http://dx.doi.org/10.4108/eetsis.v9i6.1747

Copyright © 2022 Ziquan Zhu et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.

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