A multitask learning for classification of COVID-19 variants from chest CT images using Learning without forgetting based convolution neural network

Abstract As COVID-19 spreads rapidly all over the world, the lack of reliable testing kits and medical diagnoses makes the infection more vulnerable to the human population. An effective diagnosis and detection of the SARS-Cov-2 virus are required to control and prevent the COVID-19 disease. In this study, we employed a convolution neural network (CNN) to detect coronavirus-infected patients using computed tomography (CT) images. The proposed study utilized transfer learning on the three pre-trained deep CNN models to detect COVID-19 infection from the chest CT scan images. We have tuned and optimized the hyper-parameters of the pre-trained CNN models using the Bayesian Optimization technique. Further, the deep CNN architectures are incorporated with the Learning without Forgetting (LwF) technique to improve the model’s capability to recognize new Delta variants COVID-19 data. The CNN model with the LwF is evaluated on the CT images of original and the Delta-variant COVID-19 dataset. The performance of the learning, without forgetting based CNN models namely VGG16, InceptionV3, and Xception is assessed using different performance evaluation metrics in detecting COVID-19 disease. The experimental result shows that the Xception model’s performance is superior that other two developed models and effective in classifying original augmented images and new Delta-variant images with an accuracy of 98.31% and 92.32%, respectively.The empirical result shows our model performance is significantly effective in diagnosis and classification of two different variants of the SARS-CoV-2 virus and the developed CNN models can provide assistance to the medical experts for diagnosing different variants of COVID-19 disease.