A Robust CNN Model for Diagnosis of COVID-19 Based on CT Scan Images and DL Techniques

The 2019 Coronavirus (COVID-19) virus has caused damage on people's respiratory systems over the world. Computed Tomography (CT) is a faster complement for RT-PCR during peak virus spread times. Nowadays, Deep Learning (DL) with CT provides more robust and reliable methods for classifying patte...

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Bibliographic Details
Published in:International Journal of Electronics and Telecommunications 2022-01, Vol.68 (4), p.731-739
Main Authors: Eldeeb, Ahmed H, Amr, Mohammed Nagah, Ibrahim, Amin S, Kamel, Hesham, Fouad, Sara
Format: Article
Language:English
Subjects:
Accuracy
artificial intelligence
Artificial neural networks
Classification
Computed tomography
convolutional neural networks
Coronaviruses
COVID-19
Customization
Datasets
Deep learning
Machine learning
Medical imaging
Robustness
Training
Viral diseases
Viruses
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Summary:The 2019 Coronavirus (COVID-19) virus has caused damage on people's respiratory systems over the world. Computed Tomography (CT) is a faster complement for RT-PCR during peak virus spread times. Nowadays, Deep Learning (DL) with CT provides more robust and reliable methods for classifying patterns in medical pictures. In this paper, we proposed a simple low training proposed customized Convolutional Neural Networks (CNN) customized model based on CNN architecture that layers which are optionals may be included such as the layer of batch normalization to reduce time taken for training and a layer with a dropout to deal with overfitting. We employed a huge dataset of chest CT slices images from diverse sources COVIDx-CT, which consists of a 16,146-image dataset with 810 patients of various nationalities. The proposed customized model's classification results compared to the VGG-16, Alex Net, and ResNet50 Deep Learning models. The proposed CNN model shows robustness by achieving an overall accuracy of 93% compared to 88%, 89%, and 95% for the VGG-16, Alex Net, and ResNet50 DL models for the classification of 3 classes. When this relates to binary classification, the classification accuracy of the proposed model and the VGG-16 models were identical (almost 100% accurate), with 0.17% of misclassification in the class of Non-Covid-19, the Alex Net model achieved almost 100% classification accuracy with 0.33% misclassification in the class of Non-Covid-19. Finally, ResNet50 achieved 95% classification accuracy with 5% misclassification in the Non-Covid-19 class.
ISSN:2081-8491
2300-1933
DOI:10.24425/ijet.2022.143879