<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>It is important to know how much the lungs are affected in the course of the disease in patients with COVID-19. Detecting infected tissues on CT lung images not only helps diagnose the disease but also helps measure the severity of the disease. In this paper, using the hybrid artificial intelligence-based segmentation method, which we call TA-Segnet, it has been revealed how the region with COVID-19 affects the lung on 2D CT images. A hybrid convolutional neural network-based segmentation method (TA-Segnet) has been developed for this process. We use “COVID-19 CT Lung and Infection Segmentation Dataset” and “COVID-19 CT Segmentation Dataset” to evaluate TA-SegNET. At first, the tissues with COVID-19 on each lung image are determined, then the measurements obtained are evaluated according to the parameters of Accuracy, Dice, Jaccard, Mean Square Error, Mutual Information and Cross-correlation. Accuracy, Dice, Jaccard, Mean Square Error, Mutual Information and Cross-correlation values for data set-1 are 98.63 %, 0.95, 0.919, 0.139, 0.51, and 0.904, respectively. For data set-2, these parameters are 98.57 %, 0.958, 0.992, 0.0088, 0.565 and 0.8995, respectively. Second, the ratio of COVID-19 regions relative to the lung region on CT images is determined. This ratio is compared with the values in the original data set. The results obtained show that such an artificial intelligence-based method during the pandemic period will help prioritize and automate the diagnosis of COVID-19 patients.</p>
</abstract>

It is important to know how much the lungs are affected in the course of the disease in patients with COVID-19. Detecting infected tissues on CT lung images not only helps diagnose the disease but also helps measure the severity of the disease. In this paper, using the hybrid artificial intelligence-based segmentation method, which we call TA-Segnet, it has been revealed how the region with COVID-19 affects the lung on 2D CT images. A hybrid convolutional neural network-based segmentation method (TA-Segnet) has been developed for this process. We use “COVID-19 CT Lung and Infection Segmentation Dataset” and “COVID-19 CT Segmentation Dataset” to evaluate TA-SegNET. At first, the tissues with COVID-19 on each lung image are determined, then the measurements obtained are evaluated according to the parameters of Accuracy, Dice, Jaccard, Mean Square Error, Mutual Information and Cross-correlation. Accuracy, Dice, Jaccard, Mean Square Error, Mutual Information and Cross-correlation values for data set-1 are 98.63 %, 0.95, 0.919, 0.139, 0.51, and 0.904, respectively. For data set-2, these parameters are 98.57 %, 0.958, 0.992, 0.0088, 0.565 and 0.8995, respectively. Second, the ratio of COVID-19 regions relative to the lung region on CT images is determined. This ratio is compared with the values in the original data set. The results obtained show that such an artificial intelligence-based method during the pandemic period will help prioritize and automate the diagnosis of COVID-19 patients.

Determining and Measuring the Amount of Region Having COVID-19 on Lung Images

Applied Computer Systems

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Determining and Measuring the Amount of Region Having COVID-19 on Lung Images"}

It is important to know how much the lungs are affected in the course of the disease in patients with COVID-19. Detecting infected tissues on CT lung images...

Determining and Measuring the Amount of Region Having COVID-19 on Lung Images

Publié en ligne: 30 déc. 2021

Pages: 183 - 193

DOI: https://doi.org/10.2478/acss-2021-0023

Mots clés
Image segmentation, image processing, machine learning

© 2021 Seda Arslan Tuncer et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Determining and Measuring the Amount of Region Having COVID-19 on Lung Images

Publié en ligne: 30 déc. 2021

Pages: 183 - 193

DOI: https://doi.org/10.2478/acss-2021-0023

Mots clésImage segmentation, image processing, machine learning

© 2021 Seda Arslan Tuncer et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Mots clés
Image segmentation, image processing, machine learning