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
Birincil Dil | İngilizce |
---|---|
Konular | Yapay Zeka |
Bölüm | Makaleler |
Yazarlar | |
Yayımlanma Tarihi | 31 Ağustos 2022 |
Gönderilme Tarihi | 10 Mart 2022 |
Kabul Tarihi | 19 Haziran 2022 |
Yayımlandığı Sayı | Yıl 2022Cilt: 5 Sayı: 2 |
The papers in this journal are licensed under a Creative Commons Attribution-NonCommercial 4.0 International License