COVID-19 Detection using Curvelet Transformation and Support Vector Machine

Authors

  • Sobia Yaqoob Sobia University of Okara, Pakistan
  • Arslan Akram University of Okara, Pakistan
  • Tuba Mansoor Riphah International University
  • Hirra Mustafa Sharif College of Engineering and Technology

DOI:

https://doi.org/10.56536/jicet.v3i1.55

Keywords:

Chest X-Ray, Support Vector Machine, CurveLet, Fourier Transform

Abstract

As the COVID-19 virus spreads over the globe, countries all over the world are going to extraordinary measures to combat the disease. To stop it from spreading, it's critical to have a high level of awareness and a well-thought-out COVID-19 recognition approach. By analyzing different methods and image-based detection using chest x-ray images, a technique was proposed in this study that includes preprocessing, texture feature analysis, and support vector machines. X-ray image was augmented to make equal blocks and features were extracted using Curvelet. Finally, extracted features were fed into SVM for classification. Curvelet was based on rotational and slicing texture descriptions which give the most pertinent details for the classification of COVID-19. Results in this experiment showed that the method achieved 97.7 % of accuracy against other methods based on the chest x-ray image.

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Published

2023-03-02