el 23(2): e4

Research Article

Gray Level Co-Occurrence Matrix and RVFL for Covid-19 Diagnosis

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  • @ARTICLE{10.4108/eetel.v8i2.3091,
        author={Wenhao Tang},
        title={Gray Level Co-Occurrence Matrix and RVFL for Covid-19 Diagnosis},
        journal={EAI Endorsed Transactions on e-Learning},
        keywords={COVID-19, computed tomography, gray level co-occurrence matrix, random vector function link, K-fold cross-validation},
  • Wenhao Tang
    Year: 2023
    Gray Level Co-Occurrence Matrix and RVFL for Covid-19 Diagnosis
    DOI: 10.4108/eetel.v8i2.3091
Wenhao Tang1,*
  • 1: Henan Polytechnic University
*Contact email: wenhaotang@home.hpu.edu.cn


As the widespread transmission of COVID-19 has continued to influence human health since late 2019, more intersections between artificial intelligence and the medical field have arisen. For CT images, manual differentiation between COVID-19-infected and healthy control images is not as effective and fast as AI. This study performed experiments on a dataset containing 640 samples, 320 of which were COVID-19-infected, and the rest were healthy controls. This experiment combines the gray-level co-occurrence matrix (GLCM) and random vector function link (RVFL). The role of GLCM and RVFL is to extract image features and classify images, respectively. The experimental results of my proposed GLCM-RVFL model are validated using K-fold cross-validation, and the indicators are 78.81±1.75%, 77.08±0.68%, 77.46±0.73%, 54.22±1.35%, and 77.48±0.74% for sensitivity, accuracy, F1-score, MCC, and FMI, respectively, which also confirms that the proposed model performs well on the COVID-19 detection task. After comparing with six state-of-the-art COVID-19 detection, I ensured that my model achieved higher performance.