Automated segmentation of infected regions in chest CT images of COVID-19 Patients using supervised naïve gaussian bayes classifier
Other Title(s)
التقطيع المؤتمت للمناطق المصابة في صور طبقي محوري للصدر لمرضى الكورونا COVID-19 باستخدام مصنف بايز الغاوصي المراقب
Author
Source
Tishreen University Journal for Research and Scientific Studies : Engineering Sciences Series
Issue
Vol. 42, Issue 5 (31 Oct. 2020), pp.355-365, 11 p.
Publisher
Publication Date
2020-10-31
Country of Publication
Syria
No. of Pages
11
Main Subjects
Engineering & Technology Sciences (Multidisciplinary)
Abstract EN
In this paper, one hundred chest Computed Tomography images of COVID-19 patients were used to build and test Naïve Gaussian Bayes classifier for discriminating normal from abnormal tissues.
Infected areas in these images were manually segmented by an expert radiologist.
Pixel grey value, local entropy and Histograms of Oriented Gradients HOG were extracted as features for tissue image classification.
Based on five-folds classification experiments, the accuracy score of the classifier in this fold reached around 79.94%.
Classification was more precise (85%) in recognizing normal tissue than abnormal tissue (63%).
The effectiveness in identifying positive labels was also more evident with normal tissue than the abnormal one
American Psychological Association (APA)
Hatim, Iyad Muhammad. 2020. Automated segmentation of infected regions in chest CT images of COVID-19 Patients using supervised naïve gaussian bayes classifier. Tishreen University Journal for Research and Scientific Studies : Engineering Sciences Series،Vol. 42, no. 5, pp.355-365.
https://search.emarefa.net/detail/BIM-1283161
Modern Language Association (MLA)
Hatim, Iyad Muhammad. Automated segmentation of infected regions in chest CT images of COVID-19 Patients using supervised naïve gaussian bayes classifier. Tishreen University Journal for Research and Scientific Studies : Engineering Sciences Series Vol. 42, no. 5 (2020), pp.355-365.
https://search.emarefa.net/detail/BIM-1283161
American Medical Association (AMA)
Hatim, Iyad Muhammad. Automated segmentation of infected regions in chest CT images of COVID-19 Patients using supervised naïve gaussian bayes classifier. Tishreen University Journal for Research and Scientific Studies : Engineering Sciences Series. 2020. Vol. 42, no. 5, pp.355-365.
https://search.emarefa.net/detail/BIM-1283161
Data Type
Journal Articles
Language
English
Notes
Record ID
BIM-1283161