Transfer learning-based ensemble support vector machine model for automated COVID-19 detection using lung computerized tomography scan data

Abstract

The novel discovered disease coronavirus popularly known as COVID-19 is caused due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and declared a pandemic by the World Health Organization (WHO). An early-stage detection of COVID-19 is crucial for the containment of the pandemic it has caused. In this study, a transfer learning-based COVID-19 screening technique is proposed. The motivation of this study is to design an automated system that can assist medical staff especially in areas where trained staff are outnumbered. The study investigates the potential of transfer learning-based models for automatically diagnosing diseases like COVID-19 to assist the medical force, especially in times of an outbreak. In the proposed work, a deep learning model, i.e., truncated VGG16 (Visual Geometry Group from Oxford) is implemented to screen COVID-19 CT scans. The VGG16 architecture is fine-tuned and used to extract features from CT scan images. Further principal component analysis (PCA) is used for feature selection. For the final classification, four different classifiers, namely deep convolutional neural network (DCNN), extreme learning machine (ELM), online sequential ELM, and bagging ensemble with support vector machine (SVM) are compared. The best performing classifier bagging ensemble with SVM within 385 ms achieved an accuracy of 95.7%, the precision of 95.8%, area under curve (AUC) of 0.958, and an F1 score of 95.3% on 208 test images. The results obtained on diverse datasets prove the superiority and robustness of the proposed work. A pre-processing technique has also been proposed for radiological data. The study further compares pre-trained CNN architectures and classification models against the proposed technique.

Keywords: COVID-19; CT scan data; Ensemble SVM; Transfer learning; VGG16.

Fig. 1

Self-explanatory block diagram of the proposed methodology of COVID-19 screening

Fig. 2

Pictorial representation of various stages of the pre-processing module

Fig. 3

Architecture of truncated VGG16 model

Fig. 4

Intermediate color-mapped outputs. a Layer 1. b Layer 4. c Layer 8. d Layer 14

Fig. 5

Comparision of confusion matrices before and after fine-tuning of VGG16 by evaluation on the test set with bagging SVM as the classifier

Fig. 6

Convergence graph of accuracy vs epoch for proposed methodology (VGG16+PCA+bagging ensemble with SVM)

Fig. 7

Learning curve for proposed method using 10-fold cross-validation

Fig. 8

Confusion matrices of the proposed methodology with different classifiers

Fig. 9

ROC characteristics curve for the proposed methodology (VGG16+PCA+bagging ensemble with SVM)