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%.

Deep learning models-based CT-scan image classification for automated screening of COVID-19

“…Classifier fusion method has been employed to detect COVID-19 by Kaur et al. [3] . They explored different variants of ResNet architecture and used transfer learning techniques to build the model.…”

“…This virus was found at the end of 2019 in the Wuhan province of China and critically affects the lungs of an infected person [2] . COVID-19 is the most transmissible disease caused by the SARS-CoV-2 virus [3] . COVID-19 is the name given to this new strain by the World health organization (WHO), which causes millions of fatalities worldwide.…”

“…Therefore, an automated COVID-19 screening system is needed. This will aid in faster virus diagnosis at various stages, relieving healthcare workers from the tedious process of manual annotation [3] . Consequently, researchers have presented a variety of approaches for the automated screening of COVID-19 using different scanning techniques, which are summarized in the next section.…”

A Comprehensive Review of Machine Learning Used to Combat COVID-19

“…Research in [ 87 ], examines the problem with deep learning models requiring labeled training datasets. It takes pre-trained models ResNet18, ResNet50, and ResNet101 and uses them for the automatic detection of COVID-19.…”

A Review on Deep Learning based diagnosis of COVID-19 from X-ray and CT Images

“…Kaur et al [30] proposed classifier fusion based technique. They explored transfer learning and other classifiers (SVM, KNN, LR) and all predictions.…”