Research Article
Automated Segmentation of COVID-19 Lesion from Lung CT Images Using U-Net Architecture
@INPROCEEDINGS{10.1007/978-3-030-76063-2_2, author={Seifedine Kadry and Fadi Al-Turjman and V. Rajinikanth}, title={Automated Segmentation of COVID-19 Lesion from Lung CT Images Using U-Net Architecture}, proceedings={Science and Technologies for Smart Cities. 6th EAI International Conference, SmartCity360°, Virtual Event, December 2-4, 2020, Proceedings}, proceedings_a={SMARTCITY}, year={2021}, month={5}, keywords={COVID-19 Lung CT images U-Net scheme Segmentation Performance validation}, doi={10.1007/978-3-030-76063-2_2} }- Seifedine Kadry
Fadi Al-Turjman
V. Rajinikanth
Year: 2021
Automated Segmentation of COVID-19 Lesion from Lung CT Images Using U-Net Architecture
SMARTCITY
Springer
DOI: 10.1007/978-3-030-76063-2_2
Abstract
Pneumonia caused by the novel Coronavirus Disease (COVID-19) is emerged as a global threat and considerably affected a large population globally irrespective of their age, race, and gender. Due to its rapidity and the infection rate, the World Health Organization (WHO) declared this disease as a pandemic. The proposed research work aims to develop an automated COVID-19 lesion segmentation system using the Convolutional Neural Network (CNN) architecture called the U-Net. The traditional U-Net scheme is employed to examine the COVID-19 infection present in the lung CT images. This scheme is implemented on the benchmark COVID-19 images existing in the literature (300 images) and the segmentation performance of the U-Net is confirmed by computing the essential performance measures using a relative assessment among the extracted lesion and the Ground-Truth (GT). The overall result attained with the proposed study confirms that, the U-Net scheme helps to get the better values for the performance values, such as Jaccard (>86%), Dice (>92%) and segmentation accuracy (>95%).
