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.
Primary Language | English |
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Subjects | Artificial Intelligence |
Journal Section | Articles |
Authors | |
Publication Date | August 31, 2022 |
Submission Date | March 10, 2022 |
Acceptance Date | June 19, 2022 |
Published in Issue | Year 2022Volume: 5 Issue: 2 |
The papers in this journal are licensed under a Creative Commons Attribution-NonCommercial 4.0 International License