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

By Aayush Jaiswal, Neha Gianchandani, Dilbag Singh, Vijay Kumar, Manjit Kaur. Journal of Biomolecular Structure and Dynamics, 2020

523 citations recorded by the source corpus.

Abstract 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

Subfields: Artificial Intelligence, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/c01dcde84b6dc49af8c4b9388862383a30c3e807

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In Artificial Intelligence

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

A Review of Deep Transfer Learning and Recent Advancements (2022, background): “However, if the source and target data are close enough, there is a high chance of success [16, 17, 18, 19].”
Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks (2020, background): “CAD systems based on deep learning and medical imaging are becoming more and more research fields [13, 14].”