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Journals / Sakarya University Journal of Computer and Information Sciences / 2026 / Volume: 5 Issue: 2

Application with deep learning models for COVID-19 diagnosis

Fuat Türk(ÇANKIRI KARATEKİN ÜNİVERSİTESİ)0000-0001-8159-360X
Yunus Kökver(ANKARA ÜNİVERSİTESİ)0000-0002-9864-2866
Pages
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Publication Date
2022-08-31
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The results once again demonstrate the importance of Deep Learning methods for the diagnosis of COVID-19.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:COVID-19 is a deadly virus that first appeared in late 2019 and spread rapidly around the world.
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Key Findings & Data:Understanding and classifying computed tomography images (CT) is extremely important for the diagnosis of COVID-19.
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Conclusion & Impact:The results once again demonstrate the importance of Deep Learning methods for the diagnosis of COVID-19.
🔬Design:Research Article
📍Scope:2019 Verileri
⚙️Variables:COVID-19 diagnosis · DenseNet · NasNet-Mobile

Abstract

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.

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Cite This Article

Türk, F., Kökver, Y. (2022). Application with deep learning models for COVID-19 diagnosis. Sakarya University Journal of Computer and Information Sciences, Volume: 5 Issue: 2. https://doi.org/10.35377/saucis...1085625

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