saucis

Sakarya University Journal of Computer and Information Sciences

2636-8129

Sakarya University

10.35377/saucis...1085625

Artificial Intelligence

Yapay Zeka

Application with deep learning models for COVID-19 diagnosis

https://orcid.org/0000-0001-8159-360X

Türk

Fuat

ÇANKIRI KARATEKİN ÜNİVERSİTESİ

https://orcid.org/0000-0002-9864-2866

Kökver

Yunus

ANKARA ÜNİVERSİTESİ

08 31 2022

5 2 169 180 03 10 2022 06 19 2022

2018

Sakarya University Journal of Computer and Information Sciences

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.

COVID-19 diagnosis DenseNet NasNet-Mobile deep learning classification

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