Developing a Deep Neural Network model for COVID-19 diagnosis based on CT scan images

Javad Hassannataj Joloudari; Faezeh Azizi; Issa Nodehi; Mohammad Ali Nematollahi; Fateme Kamrannejhad; Edris Hassannatajjeloudari; Roohallah Alizadehsani; Sheikh Mohammed Shariful Islam; Javad Hassannataj Joloudari; Faezeh Azizi; Issa Nodehi; Mohammad Ali Nematollahi; Fateme Kamrannejhad; Edris Hassannatajjeloudari; Roohallah Alizadehsani; Sheikh Mohammed Shariful Islam

doi:10.3934/mbe.2023725

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 9: 16236-16258. doi: 10.3934/mbe.2023725

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Research article Special Issues

Developing a Deep Neural Network model for COVID-19 diagnosis based on CT scan images

1.
Department of Computer Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran
2.
Department of Computer Engineering, University of Qom, Qom, Iran
3.
Department of Computer Sciences, Fasa University, Fasa, Iran
4.
Department of Nursing, School of Nursing and Allied Medical Sciences, Maragheh Faculty of Medical Sciences, Maragheh, Iran
5.
Institute for Intelligent Systems Research and Innovation, Deakin University, Geelong, VIC 3216, Australia
6.
Institute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Deakin University, Geelong, VIC, Australia

Received: 26 May 2023 Revised: 05 July 2023 Accepted: 13 July 2023 Published: 14 August 2023

COVID-19 is most commonly diagnosed using a testing kit but chest X-rays and computed tomography (CT) scan images have a potential role in COVID-19 diagnosis. Currently, CT diagnosis systems based on Artificial intelligence (AI) models have been used in some countries. Previous research studies used complex neural networks, which led to difficulty in network training and high computation rates. Hence, in this study, we developed the 6-layer Deep Neural Network (DNN) model for COVID-19 diagnosis based on CT scan images. The proposed DNN model is generated to improve accurate diagnostics for classifying sick and healthy persons. Also, other classification models, such as decision trees, random forests and standard neural networks, have been investigated. One of the main contributions of this study is the use of the global feature extractor operator for feature extraction from the images. Furthermore, the 10-fold cross-validation technique is utilized for partitioning the data into training, testing and validation. During the DNN training, the model is generated without dropping out of neurons in the layers. The experimental results of the lightweight DNN model demonstrated that this model has the best accuracy of 96.71% compared to the previous classification models for COVID-19 diagnosis.
- artificial intelligence,
- machine learning,
- deep neural network,
- CT scan images,
- COVID-19 diagnosis
Citation: Javad Hassannataj Joloudari, Faezeh Azizi, Issa Nodehi, Mohammad Ali Nematollahi, Fateme Kamrannejhad, Edris Hassannatajjeloudari, Roohallah Alizadehsani, Sheikh Mohammed Shariful Islam. Developing a Deep Neural Network model for COVID-19 diagnosis based on CT scan images[J]. Mathematical Biosciences and Engineering, 2023, 20(9): 16236-16258. doi: 10.3934/mbe.2023725

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Abstract

COVID-19 is most commonly diagnosed using a testing kit but chest X-rays and computed tomography (CT) scan images have a potential role in COVID-19 diagnosis. Currently, CT diagnosis systems based on Artificial intelligence (AI) models have been used in some countries. Previous research studies used complex neural networks, which led to difficulty in network training and high computation rates. Hence, in this study, we developed the 6-layer Deep Neural Network (DNN) model for COVID-19 diagnosis based on CT scan images. The proposed DNN model is generated to improve accurate diagnostics for classifying sick and healthy persons. Also, other classification models, such as decision trees, random forests and standard neural networks, have been investigated. One of the main contributions of this study is the use of the global feature extractor operator for feature extraction from the images. Furthermore, the 10-fold cross-validation technique is utilized for partitioning the data into training, testing and validation. During the DNN training, the model is generated without dropping out of neurons in the layers. The experimental results of the lightweight DNN model demonstrated that this model has the best accuracy of 96.71% compared to the previous classification models for COVID-19 diagnosis.

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