Classifier Fusion for Detection of COVID-19 from CT Scans

Abstract

The coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus. COVID-19 is found to be the most infectious disease in last few decades. This disease has infected millions of people worldwide. The inadequate availability and the limited sensitivity of the testing kits have motivated the clinicians and the scientist to use Computer Tomography (CT) scans to screen COVID-19. Recent advances in technology and the availability of deep learning approaches have proved to be very promising in detecting COVID-19 with increased accuracy. However, deep learning approaches require a huge labeled training dataset, and the current availability of benchmark COVID-19 data is still small. For the limited training data scenario, the CNN usually overfits after several iterations. Hence, in this work, we have investigated different pre-trained network architectures with transfer learning for COVID-19 detection that can work even on a small medical imaging dataset. Various variants of the pre-trained ResNet model, namely ResNet18, ResNet50, and ResNet101, are investigated in the current paper for the detection of COVID-19. The experimental results reveal that transfer learned ResNet50 model outperformed other models by achieving a recall of 98.80% and an F1-score of 98.41%. To further improvise the results, the activations from different layers of best performing model are also explored for the detection using the support vector machine, logistic regression and K-nearest neighbor classifiers. Moreover, a classifier fusion strategy is also proposed that fuses the predictions from the different classifiers via majority voting. Experimental results reveal that via using learned image features and classification fusion strategy, the recall, and F1-score have improvised to 99.20% and 99.40%.

Keywords: Activations; COVID-19; CT images; Classifier fusion; Diagnosis; Transfer learning.

Fig. 1

CT scans from the dataset COVID + ve (upper row) and non-infected by SARS-COV-2 (lower row)

Fig. 2

Visualization for transfer learning using pre-trained models [14]

Fig. 3

Proposed methodology

Fig. 4

Confusion matrix a ResNet18 b ResNet50 c ResNet101 d classification fusion

Fig. 5

Training versus epoch and loss versus epoch plot for best performing transfer learned model (ResNet50)

Fig. 6

AUC curve for best performing transfer learned model (ResNet50)

Fig. 7

Activation maps obtained via transfer learned ResNet50 model

Fig. 8

Classification fusion strategy