Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning

Deep learning models are widely used in the automatic analysis of radiological images. These techniques can train the weights of networks on large datasets as well as fine tuning the weights of pre-trained networks on small datasets. Due to the small COVID-19 dataset available, the pre-trained neural networks can be used for diagnosis of coronavirus. However, these techniques applied on chest CT image is very limited till now. Hence, the main aim of this paper to use the pre-trained deep learning architectures as an automated tool to detection and diagnosis of COVID-19 in chest CT. A DenseNet201 based deep transfer learning (DTL) is proposed to classify the patients as COVID infected or not i.e. COVID-19 (+) or COVID (-). The proposed model is utilized to extract features by using its own learned weights on the ImageNet dataset along with a convolutional neural structure. Extensive experiments are performed to evaluate the performance of the propose DTL model on COVID-19 chest CT scan images. Comparative analyses reveal that the proposed DTL based COVID-19 classification model outperforms the competitive approaches. Communicated by Ramaswamy H. Sarma

Automated summary

This study aims to utilize pre-trained deep learning architecture for the detection and diagnosis of COVID-19, proposing a Deep Transfer Learning (DTL) model based on DenseNet201. Experimental results show that the proposed model outperforms competitive approaches in COVID-19 chest CT scan images.