Application with deep learning models for COVID-19 diagnosis

التفاصيل البيبلوغرافية
العنوان:	Application with deep learning models for COVID-19 diagnosis
المؤلفون:	Yunus Kökver, Fuat Türk
المصدر:	Sakarya University Journal of Computer and Information Sciences, Vol 5, Iss 2, Pp 169-180 (2022)
بيانات النشر:	Sakarya University, 2022.
سنة النشر:	2022
المجموعة:	LCC:Electronic computers. Computer science LCC:Information technology
مصطلحات موضوعية:	covid-19 diagnosis, densenet, nasnet-mobile, deep learning classification, Electronic computers. Computer science, QA75.5-76.95, Information technology, T58.5-58.64
الوصف:	COVID-19 is a deadly virus that first appeared in late 2019 and spread rapidly around the world. Understanding and classifying computed tomography images (CT) is extremely important for the diagnosis of COVID-19. Many case classification studies face many problems, especially unbalanced and insufficient data. For this reason, deep learning methods have a great importance for the diagnosis of COVID-19. Therefore, we had the opportunity to study the architectures of NasNet-Mobile, DenseNet and Nasnet-Mobile+DenseNet with the dataset we have merged. The dataset we have merged for COVID-19 is divided into 3 separate classes: Normal, COVID-19, and Pneumonia. We obtained the accuracy 87.16%, 93.38% and 93.72% for the NasNet-Mobile, DenseNet and NasNet-Mobile+DenseNet architectures for the classification, respectively. The results once again demonstrate the importance of Deep Learning methods for the diagnosis of COVID-19.
نوع الوثيقة:	article
وصف الملف:	electronic resource
اللغة:	English
تدمد:	2636-8129
Relation:	https://dergipark.org.tr/tr/download/article-file/2301158; https://doaj.org/toc/2636-8129
DOI:	10.35377/saucis...1085625
URL الوصول:	https://doaj.org/article/969028d3854f4ad4aff8b11bf5baeb75
رقم الانضمام:	edsdoj.969028d3854f4ad4aff8b11bf5baeb75
قاعدة البيانات:	Directory of Open Access Journals

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الوصف
تدمد:	26368129
DOI:	10.35377/saucis...1085625