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Deep transfer learning - based automated detection of COVID-19 from lung CT scan slices
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  • Sakshi Ahuja ,
  • Bijaya Ketan Panigrahi ,
  • Nilanjan Dey ,
  • Tapan Gandhi ,
  • Venkatesan Rajinikanth ,
  • Tapan Kumar Gandhi
Sakshi Ahuja
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Bijaya Ketan Panigrahi
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Nilanjan Dey
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Tapan Gandhi
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Venkatesan Rajinikanth
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Tapan Kumar Gandhi
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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.