el 23(1): e3

Research Article

COVID-19 Diagnosis by Wavelet Entropy and Extreme Learning Machine

Download128 downloads
  • @ARTICLE{10.4108/eetel.v8i1.2504,
        author={Xue Han and Zuojin Hu and William Wang},
        title={COVID-19 Diagnosis by Wavelet Entropy and Extreme Learning Machine},
        journal={EAI Endorsed Transactions on e-Learning},
        keywords={COVID-19, diagnosis, Wavelet Entropy, Extreme Learning Machine, k-fold cross validation},
  • Xue Han
    Zuojin Hu
    William Wang
    Year: 2022
    COVID-19 Diagnosis by Wavelet Entropy and Extreme Learning Machine
    DOI: 10.4108/eetel.v8i1.2504
Xue Han1,*, Zuojin Hu1, William Wang2
  • 1: Nanjing Normal University of Special Education, China
  • 2: Waynesburg University
*Contact email: snow_3@163.com


In recent years, COVID-19 has spread rapidly among humans. Chest CT is an effective means of diagnosing COVID-19. However, the diagnosis of CT images still depends on the doctor's visual judgment and medical experience. This takes a certain amount of time and may lead to misjudgment. In this paper, a new algorithm for automatic diagnosis of COVID-19 based on chest CT image data was proposed. The algorithm comprehensively uses WE to extract image features, uses ELM for training, and finally passes k-fold CV validation. After evaluating and detecting performance on 296 chest CT images, our proposed method is superior to state-of-the-art approaches in terms of sensitivity, specificity, precision, accuracy, F1, MCC and FMI.