COVID-19 Detection Based on Deep Features and SVM

In the emergency context of COVID-19 and its variants, rapid and accurate diagnosis based on radiographic images is of major importance. This avoids confusion with other types of pneumonia and ensures appropriate treatment. This paper presents a hybrid model combining five pre-trained CNNs (VGG16, VGG19, MobileNet, Inception-v3 and DenseNet201) with the SVM classifier. The study was conducted on a recent public database of radiographic images of COVID-19. Experiments on 3 different patterns (COVID-19 vs Normal), (COVID-19 vs Normal vs Lung Opacity) and (COVID-19 vs Normal vs Lung Opacity vs Viral Pneumonia) showed encouraging accuracies, with higher recognition rates compared to studies using CNNs alone. In addition, the approach was validated on a separate dataset linked to Pakistani population, achieving acceptable results.