Ground-glass opacities identification using neural networks for monitoring Covid-19 progression

Abstract

COVID-19 is a worldwide pandemic since the beginning of 2020. It records a high death rate across many countries in the world. Efforts have been put in place to control the spread and the associated deaths. Vaccination, isolation, mass testing, and artificial intelligence models have been used to control the disease. Due to the high number of cases per day, manual monitoring of progression has been difficult and associated with false negatives. Ground-Glass opacities (GGOs) identification has been used in the detection and classification of COVID-19 positive and negative cases. The localization of the GGOs and the volumes in the lungs can be used to identify and monitor the COVID-19 progression in the lungs. This research developed an adoptive computational model to help in COVID-19 monitoring. It identifies the localization of GGOs in the lungs responsible for COVID-19. The research also used automated feature extraction convolutional neural networks (CNN) models to enhance speed and accuracy. Feature extraction and modeling were done with standard CNN and CNN with transfer learning with augmentation models. CNN with the transfer learning model was chosen for the implementation because of the high accuracy of 97.36%. The model was used to identify GGOs given new examples to classify COVID-19 positive and negative cases accurately.