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• W3040660552 abstract "AbstractDeep 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. SarmaKeywords: COVID-19classificationdeep learningdeep transfer learning Disclosure statementThe authors declare no conflict of interest regarding the publication of this paper.Ethical approvalThis research work does not involve chemicals, procedures or equipment that have any unusual hazards inherent in their use." @default.
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• W3040660552 date "2020-07-03" @default.
• W3040660552 modified "2023-10-09" @default.
• W3040660552 title "Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning" @default.
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• W3040660552 doi "https://doi.org/10.1080/07391102.2020.1788642" @default.
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