Compressive Sensing-Based Computed Tomography Imaging: An effective approach for COVID-19 Detection

Abstract

We know that COVID-19 has been considered a pandemic and various types of symptoms are analyzed by the doctors. Various cases belonging to COVID-19 are asymptomatic and due to this fact the disease is not analyzed at an initial stage and the condition of the patient will be critical. So, the purpose of this work is to provide a solution that will find out the highly precise test result of COVID-19. Magnetic Resonance Imaging, CT Scan & Lung Ultrasound are some of the methods which can provide the exact results for the testing. But the problem associated with these imaging modalities is that they are time-consuming and the data provided by these modalities are large enough to store or transmit. A compression technique is required which can reduce the time as well as data size. Computed Tomography with Compressive Sensing (CS) Technique is used as an approach to tackle the above-stated problem. To analyze the fact that this technique is efficient, we consider the Computed Tomography-based Chest images of COVID-19 infected patients and apply the CS technique (Basis Pursuit) with Discrete Cosine Transform as a representation basis and Gaussian as a measurement matrix. As a result of this study, we find out three parameters, PSNR, SSIM & FSIM, to visualize the efficiency of the reconstruction strategy. This work concludes that the Computed Tomography approach with the help of CS can be used for fast and efficient imaging for COVID-19 as well as other diseases of the same kind.