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

• DOI: 10.1016/j.sasc.2024.200077
• Publication Date: 2024-02-04T06:44:09Z
• Authors: Muhammad Minoar H...; Md. Abul Ala Walid; S.M. Saklain Galib; Mir Mohammad Azad; Wahidur Rahman; A.S.M. Shafi; Mohammad Motiur R...
• Keywords: 2019-20 coronavirus outbreak

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