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Covid-19 Classification Based on Gray-Level Co-occurrence Matrix and Support Vector Machine

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COVID-19: Prediction, Decision-Making, and its Impacts

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 60))

Abstract

Covid-19 is a new epidemic recently. Early diagnosis of related diseases relies on the analysis of the patient’s clinical symptoms and kit testing. To identify this disease efficiently and automatically, we proposed an effective classification system by identifying CT images of chest based on Histogram Equalization (HE), Gray-Level Co-Occurrence Matrix (GLCM) and Support Vector Machine (SVM) algorithm. We collected 148 CT images of healthy people and 148 CT images of patients as our first-hand dataset, the size of which is 512*512*3. To enhance the features of the images, we center cropped the images to 400*400*3. GLCM is an efficient method to extract features focusing on the texture features and SVM can be accurately utilized to classify. In our experiment, we proposed a 10-fold Cross-Validation (CV) to ensure the reliability of experimental results. The results show that the average accuracy of our system is better than other common methods. The performance of our proposed method is effective for Covid-19 identification.

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Authors and Affiliations

  1. School of Informatics, University of Leicester, Leicester, LE1 7RH, UK

    Yihao Chen

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  1. Yihao Chen
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Correspondence to Yihao Chen .

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Editors and Affiliations

  1. The University of South Dakota, Vermillion, SD, USA

    K.C. Santosh

  2. Global Knowledge Research Foundation, Ahmedabad, India

    Amit Joshi

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Chen, Y. (2021). Covid-19 Classification Based on Gray-Level Co-occurrence Matrix and Support Vector Machine. In: Santosh, K., Joshi, A. (eds) COVID-19: Prediction, Decision-Making, and its Impacts. Lecture Notes on Data Engineering and Communications Technologies, vol 60. Springer, Singapore. https://doi.org/10.1007/978-981-15-9682-7_6

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