Expanding new COVID-19 data with conditional generative adversarial networks
المؤلفون المشاركون
Majid, Hanin
Ali, Khawlah Husayn
المصدر
The Iraqi Journal of Electrical and Electronic Engineering
العدد
المجلد 18، العدد 1 (30 يونيو/حزيران 2022)، ص ص. 103-110، 8ص.
الناشر
تاريخ النشر
2022-06-30
دولة النشر
العراق
عدد الصفحات
8
التخصصات الرئيسية
تكنولوجيا المعلومات وعلم الحاسوب
الموضوعات
الملخص EN
COVID-19 is an infectious viral disease that mostly affects the lungs.
that quickly spreads across the world.
early detection of the virus boosts the chances of patients recovering quickly worldwide.
many radiographic techniques are used to diagnose an infected person such as X-rays, deep learning technology based on a large amount of chest x-ray images is used to diagnose COVID-19 disease.
because of the scarcity of available COVID-19 X-rays image, the limited COVID-19 Datasets are insufficient for efficient deep learning detection models.
another problem with a limited dataset is that training models suffer from over-fitting, and the predictions are not generalizable to address these problems.
in this paper, we developed conditional generative adversarial networks (CGAN) to produce synthetic images close to real images for the COVID-19 case and traditional augmentation that was used to expand the limited dataset then used to train by Customized deep detection model.
the customized deep learning model was able to obtain excellent detection accuracy of 97% accurate with only ten epochs.
the proposed augmentation outperforms other augmentation techniques.
the augmented dataset includes 6988 high-quality and resolution COVID-19 X-rays images.
at the same time, the original COVID-19 X-rays images are only 587.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Majid, Hanin& Ali, Khawlah Husayn. 2022. Expanding new COVID-19 data with conditional generative adversarial networks. The Iraqi Journal of Electrical and Electronic Engineering،Vol. 18, no. 1, pp.103-110.
https://search.emarefa.net/detail/BIM-1380214
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Majid, Hanin& Ali, Khawlah Husayn. Expanding new COVID-19 data with conditional generative adversarial networks. The Iraqi Journal of Electrical and Electronic Engineering Vol. 18, no. 1 (Jun. 2022), pp.103-110.
https://search.emarefa.net/detail/BIM-1380214
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Majid, Hanin& Ali, Khawlah Husayn. Expanding new COVID-19 data with conditional generative adversarial networks. The Iraqi Journal of Electrical and Electronic Engineering. 2022. Vol. 18, no. 1, pp.103-110.
https://search.emarefa.net/detail/BIM-1380214
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references : p. 109-110
رقم السجل
BIM-1380214
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر