el 21(22): e3

Research Article

Gray level co-occurrence matrix and Schmitt neural network for Covid-19 diagnosis

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  • @ARTICLE{10.4108/eai.11-8-2021.170668,
        author={Pengpeng Pi},
        title={Gray level co-occurrence matrix and Schmitt neural  network for Covid-19 diagnosis},
        journal={EAI Endorsed Transactions on e-Learning},
        volume={7},
        number={22},
        publisher={EAI},
        journal_a={EL},
        year={2021},
        month={8},
        keywords={Machine Learning, Schmitt neural network, SARS-CoV-2},
        doi={10.4108/eai.11-8-2021.170668}
    }
    
  • Pengpeng Pi
    Year: 2021
    Gray level co-occurrence matrix and Schmitt neural network for Covid-19 diagnosis
    EL
    EAI
    DOI: 10.4108/eai.11-8-2021.170668
Pengpeng Pi1,*
  • 1: College of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, Henan 454000, P R China
*Contact email: pipengpeng@home.hpu.edu.cn

Abstract

INTRODUCTION: When COVID-19 spreads to most of the world, chest CT imaging is widely regarded as a convenient and feasible method for the diagnosis of suspected patients. In the traditional diagnosis method, doctors and experts judge these CT images and draw conclusions. However, with the surge in the number of suspected patients, relying solely on traditional manual diagnosis methods can no longer meet people's demand for efficiency.

OBJECTIVES: A number of previous studies have shown that it is possible to use machine learning methods to help people diagnose suspected SARS-CoV-2 patients. However, the accuracy of the existing scheme still needs to be improved.

METHODS: In order to make a more accurate diagnosis of suspected patients with SARS-CoV-2, we proposed a new model. We first preprocessed the CCT images of the tested objects to seek for higher accuracy, then extracted the texture features from the processed CCT images, and finally divided the CCT images of the tested objects into two categories: sick and normal using Schmidt neural network.

RESULTS: The accuracy of the proposed model is (76.33±1.18%), while the accuracy of the existing model RBFNN, ELM-BA, WEBBO and GLCM-SVM are (73.45±0.69%), (74.88±0.86%), (70.48±0.81%) and (64.42±0.88%), respectively. Compared with the existing RBFNN, ELM-BA, WEBBO and GLCM-SVM models, the accuracy of our proposed model is 1.45% higher than that of the best ELM-BA model. More importantly, the proposed model has better stability.

CONCLUSION: The model we proposed is feasible for the corresponding diagnosis of suspected patients. This is not only conducive to timely treatment of patients, but more importantly, effective isolation of confirmed patients as soon as possible can prevent the further spread of the epidemic.