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