Edge impulse-based pretrained neural network for diagnosing COVID-19

Research Article
Open access

Edge impulse-based pretrained neural network for diagnosing COVID-19

Wenbin Gao 1*
  • 1 University of Leeds    
  • *corresponding author ml21w2g@leeds.ac.uk
Published on 22 February 2024 | https://doi.org/10.54254/2755-2721/41/20230753
ACE Vol.41
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-307-4
ISBN (Online): 978-1-83558-308-1

Abstract

COVID-19 has wreaked havoc on a global scale, primarily owing to its extraordinary contagiousness, thereby straining local healthcare systems to their limits. While the Reverse Transcription Polymerase Chain Reaction (RT-PCR) test is known for its specificity, it suffers from time-consuming procedures and a notable false negative rate. Consequently, there is an immediate imperative for a swift and precise diagnostic approach. This paper introduces a novel concept, employing artificial intelligence, to address these challenges effectively. Specifically, this study employed a transfer learning model provided by the Edge Impulse platform and a dataset containing chest X-ray images of children. This study pre-processed these images and trained and tested them several times using different image sizes and network architectures. The experimental results show that the model achieves very high accuracy (>99%) with 160*160 image size and version 1.0 or 0.35 of the network architecture. These results clearly support the hypothesis that migration learning can play an important role in the fast and accurate diagnosis of COVID-19 with appropriate image size and network architecture. This research can be used as a way to rapidly train locally adapted AI models to achieve rapid assisted diagnosis of this type of acute infectious disease.

Keywords:

COVID-19, Artificial Intelligence, Transfer Learning, Edge Impulse

Gao,W. (2024). Edge impulse-based pretrained neural network for diagnosing COVID-19. Applied and Computational Engineering,41,215-221.
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References

[1]. Suri J S et al 2021 A narrative review on characterization of acute respiratory distress syndrome in COVID-19-infected lungs using artificial intelligence Computers in Biology and Medicine 130 104210

[2]. World Health Organization 2023 https://covid19.who.int/

[3]. Safiabadi T Seyed H et al 2021 Tools and techniques for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)/COVID-19 detection Clinical microbiology reviews 34.3 10-1128.

[4]. Kukar M et al 2021 COVID-19 diagnosis by routine blood tests using machine learning Scientific reports 11.1 10738

[5]. Domínguez O Juan L et al. 2021 Machine learning applied to clinical laboratory data in Spain for COVID-19 outcome prediction: model development and validation Journal of medical Internet research 23.4 e26211

[6]. Li L et al 2020 Using artificial intelligence to detect COVID-19 and community-acquired pneumonia based on pulmonary CT: evaluation of the diagnostic accuracy Radiology 296.2 E65-E71

[7]. Singh T et al 2022 Ftl-CoV19: A Transfer Learning Approach to Detect COVID-19 Computational Intelligence and Neuroscience 2022

[8]. Maior C B S et al. 2021 Convolutional neural network model based on radiological images to support COVID-19 diagnosis: Evaluating database biases Plos one 16.3 e0247839.

[9]. Qiu Y et al 2019 Semantic segmentation of intracranial hemorrhages in head CT scans In 2019 IEEE 10th International Conference on Software Engineering and Service Science (ICSESS) pp 112-115) IEEE

[10]. Lehečka J Psutka J V and Psutka J 2023 Transfer Learning of Transformer-based Speech Recognition Models from Czech to Slovak arXiv preprint arXiv:2306.04399

Cite this article

Gao,W. (2024). Edge impulse-based pretrained neural network for diagnosing COVID-19. Applied and Computational Engineering,41,215-221.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2023 International Conference on Machine Learning and Automation

ISBN：978-1-83558-307-4(Print) / 978-1-83558-308-1(Online)
Editor：Mustafa İSTANBULLU
Conference website: https://2023.confmla.org/
Conference date: 18 October 2023
Series: Applied and Computational Engineering
Volume number: Vol.41
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Suri J S et al 2021 A narrative review on characterization of acute respiratory distress syndrome in COVID-19-infected lungs using artificial intelligence Computers in Biology and Medicine 130 104210

[2]. World Health Organization 2023 https://covid19.who.int/

[3]. Safiabadi T Seyed H et al 2021 Tools and techniques for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)/COVID-19 detection Clinical microbiology reviews 34.3 10-1128.

[4]. Kukar M et al 2021 COVID-19 diagnosis by routine blood tests using machine learning Scientific reports 11.1 10738

[5]. Domínguez O Juan L et al. 2021 Machine learning applied to clinical laboratory data in Spain for COVID-19 outcome prediction: model development and validation Journal of medical Internet research 23.4 e26211

[6]. Li L et al 2020 Using artificial intelligence to detect COVID-19 and community-acquired pneumonia based on pulmonary CT: evaluation of the diagnostic accuracy Radiology 296.2 E65-E71

[7]. Singh T et al 2022 Ftl-CoV19: A Transfer Learning Approach to Detect COVID-19 Computational Intelligence and Neuroscience 2022

[8]. Maior C B S et al. 2021 Convolutional neural network model based on radiological images to support COVID-19 diagnosis: Evaluating database biases Plos one 16.3 e0247839.

[9]. Qiu Y et al 2019 Semantic segmentation of intracranial hemorrhages in head CT scans In 2019 IEEE 10th International Conference on Software Engineering and Service Science (ICSESS) pp 112-115) IEEE

[10]. Lehečka J Psutka J V and Psutka J 2023 Transfer Learning of Transformer-based Speech Recognition Models from Czech to Slovak arXiv preprint arXiv:2306.04399

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