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Compressed feature vector-based effective object recognition model in detection of COVID-19.

Chao Chen1, Jinhong Mao2, Xinzhi Liu2

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This study introduces a novel COVID-19 recognition model using compressed feature vectors for faster and accurate detection. The approach optimizes support vector machines, improving efficiency without sacrificing performance.

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Medical Diagnostics

Background:

  • Object recognition is crucial in computer vision, but current methods often use complex features leading to inefficiency.
  • Existing approaches for COVID-19 recognition may lack speed despite high accuracy.

Purpose of the Study:

  • To develop an effective and efficient recognition model for COVID-19 detection.
  • To improve recognition speed without compromising accuracy.

Main Methods:

  • A compressed feature vector is proposed using compressive sensing and a sparse matrix to reduce dimensionality.
  • An optimized kernel support vector machine (SVM) model is developed by reducing support vectors for enhanced inference efficiency.
  • The SVM model utilizes patient data like age and gender to predict COVID-19 infection.

Main Results:

  • The compressed feature vector approach significantly reduces computation complexity while retaining essential information.
  • The optimized SVM model achieves comparable recognition accuracy to the original kernel SVM but with greatly improved recognition time.
  • Experimental results on two datasets demonstrate favorable performance in both accuracy and speed.

Conclusions:

  • The proposed model offers an efficient solution for COVID-19 recognition, balancing speed and accuracy.
  • The method highlights the potential of compressed sensing and SVM optimization in medical image analysis and diagnostics.