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

Ziquan Zhu, Shui Hua Wang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Article numbere4
JournalEAI Endorsed Transactions on Scalable Information Systems
Issue number2
Publication statusPublished - 2023
Externally publishedYes


  • ResNet50
  • bat algorithm
  • breast cancer
  • extreme learning machine
  • ultrasound image


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