A Machine learning Classification approach for detection of Covid 19 using CT images

  • Suguna G C JSS Academy Of Technical Education
  • Veerabhadrappa S T JSS Academy of Technical Education Bengaluru, India
  • Tejas A JSS Academy of Technical Education Bengaluru, India
  • Vaishnavi P JSS Academy of Technical Education Bengaluru, India
  • Raghunandan Gowda JSS Academy of Technical Education Bengaluru, India
  • Panchami Udupa JSS Academy of Technical Education Bengaluru, India
  • Spoorthy JSS Academy of Technical Education Bengaluru, India
  • Smitha Reddy JSS Academy of Technical Education Bengaluru, India
  • Sudarshan E JSS Academy of Technical Education Bengaluru, India
Keywords: Covid, SVM, Random Forest, Computed Tomography, GLCM


Coronavirus disease 2019 popularly known as COVID 19 was first found in Wuhan, China in December 2019. World Health Organization declared Covid 19 as a transmission disease. The symptoms were cough, loss of taste, fever, tiredness, respiratory problem. These symptoms were likely to show within 11 –14 days. The RT-PCR and rapid antigen biochemical tests were done for the detection of COVID 19. In addition to biochemical tests, X-Ray and Computed Tomography (CT) images are used for the minute details of the severity of the disease. To enhance efficiency and accuracy of analysis/detection of COVID images and to reduce of doctors' time for analysis could be addressed through Artificial Intelligence. The dataset from Kaggle was utilized to analyze. The statistical and GLCM features were extracted from CT images for the classification of COVID and NON-COVID instances in this study. CT images were used to extract statistical and GLCM features for categorization. In the proposed/prototype model, we achieved the classification accuracy of 91%, and 94.5% using SVM and Random Forest respectively.


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How to Cite
G C, S., S T, V., A, T., P, V., Gowda, R., Udupa, P., Spoorthy, Reddy, S., & E, S. (2022). A Machine learning Classification approach for detection of Covid 19 using CT images. EMITTER International Journal of Engineering Technology, 10(1), 183-194. https://doi.org/10.24003/emitter.v10i1.672