International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

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 Volume 9, Issue 4 (April 2022), Pages: 44-52

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 Original Research Paper

 Title: Deep transfer learning CNN based approach for COVID-19 detection

 Author(s): Wazir Muhammad 1, *, Zuhaibuddin Bhutto 2, Syed Ali Raza Shah 3, Jalal Shah 2, Murtaza Hussain Shaikh 4, Ayaz Hussain 1, Imdadullah Thaheem 5, Shamshad Ali 1

 Affiliation(s):

 1Department of Electrical Engineering, Balochistan University of Engineering and Technology, Khuzdar, Pakistan
 2Department of Computer System Engineering, Balochistan University of Engineering and Technology, Khuzdar, Pakistan
 3Department of Mechanical Engineering, Balochistan University of Engineering and Technology, Khuzdar, Pakistan
 4Department of Information Systems, Kyungsung University, Busan, South Korea
 5Department of Energy Systems Engineering, Balochistan University of Engineering and Technology, Khuzdar, Pakistan

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 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0002-3860-2213

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2022.04.006

 Abstract:

Recently, the novel coronavirus (Covid-19) and its different variants have spread rapidly across the world. Early-stage detection of COVID-19 is a challenging task due to the limited availability of Covid testing kits to the public. Conventionally, reverse transcription-polymerase chain reaction (RT-PCR) is the reliable test for the detection of COVID-19 which is time-consuming and costly. The aim of this work is to identify the COVID-19 symptoms with the help of a deep learning algorithm using chest X-Ray images. In order to improve the quality of chest X-Ray images, authors have further modified the pre-trained model with some extra CNN layers, such as the first layer is the average pooling layer and the other two are dense layers followed by ReLU with softmax activation function. The experimental results have been carried out on publicly available chest X-Ray images of COVID-19 to mark COVID-19 patients as positive and negative datasets. For evaluation purpose, we have used benchmark of pre-trained models such as VGG-16 (Visual Geometry Group), VGG19, Xception, ResNet152, ResNet152v2, ResNet101, ResNet101v2, DenseNet201, DenseNet169 and DenseNet121. On the benchmark datasets, viz. COVID-19 X-Ray images, an average improvement in terms of training/validation accuracy, precision, recall, and F1-scores scores were 95%, 94%, 99/88%, 99/88%, and 93/92% respectively. The results provide sufficient evidence that deep learning can be used efficiently for the detection of COVID-19 symptoms. 

 © 2022 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: COVID-19, Deep learning, Transfer learning, Chest X-ray

 Article History: Received 20 September 2021, Received in revised form 31 January 2022, Accepted 7 February 2022

 Acknowledgment 

This work was supported by the Baluchistan University of Engineering and Technology, Khuzdar.

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Muhammad W, Bhutto Z, and Shah SAR et al. (2022). Deep transfer learning CNN based approach for COVID-19 detection. International Journal of Advanced and Applied Sciences, 9(4): 44-52

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 Figures

 Fig. 1 Fig. 2 Fig. 3 Fig. 4

 Tables

 Table 1 Table 2 Table 3   

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