Designing a convolutional neural networks model to predict Covid-19 disease

Authors

  • AOUS Mohamad Tishreen University
  • Ghada Saad Tishreen University

Keywords:

convolutional neural networks , X-ray images, CT images, Canser, covid19.

Abstract

In this research, a multi-classification system was designed to automatically detect Covid-19 disease and lung cancer based on convolutional neural networks, through two data sets, the first is computed tomography images of both infected and uninfected people, and the second is x-ray images, and we divided these data into two groups. (Test - Training), and then we built and trained the model and compared the performance of the system between the use of the first group (computed tomography images), which gave an accuracy of 98%, and the second group (X-ray images), which gave an accuracy of 86%, and therefore the use of computed tomography images gives better results. In diagnosing the disease, the results of our designed system also show better accuracy than the results of previous reference studies that used Alex Net, VGG19, Res Net, Google Net, Squeeze Net, which give the following accuracy, respectively, when they are used for X-ray images (58.62, 67.74, 84.48, 75.86, 70.68) and gives the following resolution when used for CT images (89.1, 93.10, 93.10, 89.65, 82.75).

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Published

2023-02-27

How to Cite

1.
محمد أ, غادة سعد. Designing a convolutional neural networks model to predict Covid-19 disease. Tuj-eng [Internet]. 2023Feb.27 [cited 2024May2];44(6):185-201. Available from: https://journal.tishreen.edu.sy/index.php/engscnc/article/view/12467

Issue

Vol. 44 No. 6 (2022): Tishreen University Journal of Research and Scientific Studies - Engineering Sciences Series

Section

Articles

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Copyright (c) 2023 ttps://creativecommons.org/licenses/by-nc-sa/4.0/

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