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Journal of Industrial and Systems Engineering، جلد ۱۳، شماره ۳، صفحات ۶۱-۶۹
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عنوان انگلیسی A quantitative scoring system to compare the degree of COVID-19 infection in patients’ lungs during the three peaks of the pandemic in Iran
چکیده انگلیسی مقاله Contrary to most countries, coronavirus has peaked three times during the last eight months in Iran. Unfortunately, increasing the number of positive COVID-19 cases is not the only crucial problem we faced. Indeed, it seems that lung coronavirus infections have also become more severe during these three peaks. Therefore, this study proposes a quantitative scoring system based on medical imaging to score the degree of lung coronavirus infection during each peak. Regarding the degree of lung coronavirus infection for all patients during the last three peaks, we test the mentioned hypothesis by employing statistical methods. Comparing the characteristics of the disease during three different peaks is another goal of the research. To this end, 5265 lung CT scan images from 270 patients with a definite diagnosis of COVID-19 infection were annotated under radiology expert supervision. Then, was used deep learning methods for image segmentation. In the next step, each patient’s lung was divided into six sections, and the percentage of infection was calculated in each section. Finally, the Friedman and Games-Howell tests showed that the average degree of COVID-19 infection has increased during the considered period, and the average of infection in men was about twenty percent higher than in women.
کلیدواژه‌های انگلیسی مقاله COVID-19,CT scan,statistical analysis,Deep Learning,Image Segmentation
نویسندگان مقاله AmirHossein Masoumi |
School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran

Rouzbeh Ghousi |
School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran

Mozhgan Vazifehdoost |
Faculty of Industrial Engineering and Management Systems, Amirkabir University of Technology, Tehran, Iran

Faeze Araghi Niknam |
School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran

نشانی اینترنتی https://www.jise.ir/article_129268_f870b1060fd389425804afe2e0b8e75b.pdf
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