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Register a new userAutomated Segmentation of Infected Regions in Chest CT Images of COVID-19 Patients using Supervised Naïve Gaussian Bayes Classifier
التقطيع المؤتمت للمناطق المصابة في صور طبقي محوري للصدر لمرضى الكورونا COVID-19 باستخدام مصنف بايز الغاوصي المراقب
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Tishreen University ورقة بحثية
Publication date
2020
fields
Informatics Engineering
and research's language is
العربية
Authors
إياد حاتم( باحث )
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Shamra Editor
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استخدمت في هذه الورقة مائة صورة من تصوير مقطع محوسب للصدر لمرضى الكورونا لبناء واختبار مصنف بايز الغوصي لتمييز النسج الطبيعية من النسج الغير طبيعية. قسمت المناطق المصابة في هذه الصور يدويا بواسطة اخصائي اشعة خبير. أجريت عملية استخراج قيمة البكسل الرمادية والانتروبيا المحلية وقيم الهستوغرام للتدرجات الموجهة كميزات لتصنيف صور الانسجة. استنادا إلى تجارب تصنيف ذات خمس طيات، وصلت درجة دقة المصنف إلى حوالي 79.94%. كان التصنيف أكثر دقة (٨٥٪) في التعرف على الأنسجة الطبيعية من الأنسجة غير الطبيعية (٦٣٪). كانت أيضا الفعالية في تحديد المناطق أكثر وضوحاً في الأنسجة غير الطبيعية.
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.
References used
Marina Sokolova, Guy Lapalme, A systematic analysis of performance measures for classification tasks, Information Processing & Management, Volume 45, Issue 4, 2009, Pages 427-437, ISSN 0306-4573
Danny Petschke, Torsten E.M. Staab, A supervised machine learning approach using NaiveGaussian Bayes classification for shape-sensitive detector pulse discrimination in positron annihilation lifetime spectroscopy (PALS), Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Volume 947, 2019, 162742, ISSN 0168-9002
X. Zhang, F. An, I. Nakashima, A. Luo, L. Chen, I. Ishii, H.J. Mattausch, A hardware-oriented histogram of oriented gradients algorithm and its VLSI implementation, Japanese Journal of Applied Physics, 56 (2017) 04CF01
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