Detection of COVID-19 lesions based on computed tomography using U-Net 2.5D and GAN

Abstract

This paper proposes a computational method for automatically detecting suspected regions of COVID-19 from CT scans. COVID-19 has spread rapidly worldwide, infecting over 462 million people and causing over 6 million deaths. There are various methods to diagnose COVID-19, including imaging. The proposed method has five stages, including image acquisition, pre-processing, lung extraction, segmentation of suspected regions using U-Net 2.5D and Pix2Pix architectures, and result validation. The method achieved promising results, with 92% Dice for lung parenchyma segmentation, 82% Dice for suspected region segmentation using U-Net, and 71% Dice using Pix2Pix. It could potentially be integrated into clinical environments as a real aid system.