Segmentation of Chest X-Ray Images Using U-Net Model

Kamil, Mohammed Y.; Hashem, Sahar A.

Segmentation of Chest X-Ray Images Using U-Net Model

Date

2022-12-20

Authors

Kamil, Mohammed Y.

Hashem, Sahar A.

Publisher

Institute of Automation and Computer Science, Brno University of Technology

Altmetrics

Abstract

Medical imaging, such as chest X-rays, gives an acceptable image of lung functions.  Manipulating these images by a radiologist is difficult, thus delaying the diagnosis. Coronavirus is a disease that affects the lung area. Lung segmentation has a significant function in assessing lung disorders. The process of segmentation has seen widespread use of deep learning algorithms. The U-Net is one of the most significant semantic segmentation frameworks for a convolutional neural network. In this paper, the proposed U-Net architecture is evaluated on 565 datasets divided into 500 training images and 65 validation images, For chest X-ray. The findings of the experiments demonstrate that the suggested strategy successfully achieved competitive outcomes with 91.47% and 89.18% accuracy, 0.7494 and 0.7480 IoU, 19.23% and 26.11% loss for training and validation images, respectively.

Keywords

U-Net, Segmentation, Deep learning, Coronavirus, lung, X-ray, CNN

Citation

Mendel. 2022 vol. 28, č. 2, s. 49-53. ISSN 1803-3814
https://mendel-journal.org/index.php/mendel/article/view/192

Document type

Peer-reviewed

Document version

Published version

Language of document

en

Document licence

Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International license
http://creativecommons.org/licenses/by-nc-sa/4.0