COVID-19 Contamination Extraction From CT Images Using an Adaptive Network

Abstract

The COVID-19 pandemic is one of the most significant threats to the general population's health in the 21st century. In this study, a novel meta-learning based FSS model is proposed. This model is realized as an adaptive relation network built on Deeplabv3+ for training the support sets and a convolutional network with swish activations functions for non-linear metric learning. The performance of this model that was trained using supervised and semi-supervised learning algorithms on two public datasets is significantly better. This model obtains a global accuracy of 0.8396 for ground glass opacity (GGO) and consolidation segmentation and 0.9996 for entire lung infection segmentations correspondingly. In addition, the model that was proposed generalizes well with data that has not yet been seen and has the potential to be expanded to the identification of other infections in image volumes that are rendered in three dimensions and four dimensions.