COVID-19 detection from chest CT images using optimized deep features and ensemble classification

Abstract

Diagnosis of COVID-19 positive patients is the eventual move to impede the expansion of coronavirus. Variations of coronavirus make it tough to recognize COVID-19 positive patients through symptoms. Hence, this research aims at a faster and automatic detection approach of COVID-19 disease from the chest Computed tomography (CT) scan images. For the composition of the system, this approach constructs a feature vector from the CT images through the features fusion of two Convolutional neural network (CNN) models namely VGG-19 and ResNet-50. Before the feature fusion, preprocessing techniques are applied to gain more accurate outcomes. Moreover, pertinent features are identified from the feature vector by using several feature optimization methods namely Recursive feature elimination (RFE), Principal component analysis (PCA), and Linear discriminant analysis (LDA), and among them, we have observed PCA as the best preference. Classification is performed on the optimized feature utilizing the Max voting ensemble classification (MVEC). The fused features of VGG-19 and ResNet-50, processed with PCA and MVEC, provide the best outcomes of accuracy, specificity, sensitivity, and precision at 98.51 %, 97.58 %, 99.49 %, and 97.47 %, respectively, after 5-fold cross-validation for the proposed method.