The SARS-CoV-2 is quickly spreading worldwide resulting in millions of infection and death cases. As a consequence, it is increasingly important to diagnose the presence of COVID-19 infection regardless of the technique applied. To this end, this work deals with the problem of COVID-19 classification using Computed Tomography (CT) images. Precisely, a new feature-based approach is proposed by exploiting axial CT lung acquisitions in order to differentiate COVID-19 versus healthy Computed Tomography (CT) images. In particular, first-order statistical measures as well as numerical quantities extracted from the autocorrelation function are investigated with the aim to provide an efficient classification process ensuring satisfactory performance results.
COVID-19 Lung CT Images Recognition: A Feature-Based Approach
Pallotta L.;
2021-01-01
Abstract
The SARS-CoV-2 is quickly spreading worldwide resulting in millions of infection and death cases. As a consequence, it is increasingly important to diagnose the presence of COVID-19 infection regardless of the technique applied. To this end, this work deals with the problem of COVID-19 classification using Computed Tomography (CT) images. Precisely, a new feature-based approach is proposed by exploiting axial CT lung acquisitions in order to differentiate COVID-19 versus healthy Computed Tomography (CT) images. In particular, first-order statistical measures as well as numerical quantities extracted from the autocorrelation function are investigated with the aim to provide an efficient classification process ensuring satisfactory performance results.File | Dimensione | Formato | |
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59_Losquadro_Ciarp_2021_Covid-19_Lung_CT_Images.pdf
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