SGS: SqueezeNet-guided Gaussian-kernel SVM for COVID-19 Diagnosis

Abstract

The ongoing global pandemic has underscored the importance of rapid and reliable identification of COVID-19 cases to enable effective disease management and control. Traditional diagnostic methods, while valuable, often have limitations in terms of time, resources, and accuracy. The approach involved combining the SqueezeNet deep neural network with the Gaussian kernel in support vector machines (SVMs). The model was trained and evaluated on a dataset of CT images, leveraging SqueezeNet for feature extraction and the Gaussian kernel for non-linear classification. The SN-guided Gaussian-Kernel SVM (SGS) model achieved high accuracy and sensitivity in diagnosing COVID-19. It outperformed other models with an impressive accuracy of 96.15% and exhibited robust diagnostic capabilities. The SGS model presents a promising approach for accurate COVID-19 diagnosis. Integrating SqueezeNet and the Gaussian kernel enhances its ability to capture complex relationships and classify COVID-19 cases effectively.