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Deep transfer learning - based automated detection of COVID-19 from lung CT scan slices

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posted on 28.05.2020 by Sakshi Ahuja, Bijaya Ketan Panigrahi, Nilanjan Dey, Venkatesan Rajinikanth, Tapan Kumar Gandhi
In the proposed research work; the COVID-19 is detected using transfer learning from CT scan images decomposed to three-level using stationary wavelet. A three-phase detection model is proposed to improve the detection accuracy and the procedures are as follows; Phase1- data augmentation using stationary wavelets, Phase2- COVID-19 detection using pre-trained CNN model and Phase3- abnormality localization in CT scan images. This work has considered the well known pre-trained architectures, such as ResNet18, ResNet50, ResNet101, and SqueezeNet for the experimental evaluation. In this work, 70% of images are considered to train the network and 30% images are considered to validate the network. The performance of the considered architectures is evaluated by computing the common performance measures.

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Email Address of Submitting Author

Sakshi.Ahuja@ee.iitd.ac.in

ORCID of Submitting Author

0000-0002-7149-1623

Submitting Author's Institution

Indian Institute of Technology Delhi

Submitting Author's Country

India

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