Hu, Qinhua, Gois, Francisco Nauber B, Costa, Rafael, Zhang, Lijuan, Yin, Ling, Magaia, Naercio and de Albuquerque, Victor Hugo C (2022) Explainable artificial intelligence-based edge fuzzy images for COVID-19 detection and identification. Applied Soft Computing, 123. a108966 1-19. ISSN 1568-4946

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Abstract

The COVID-19 pandemic continues to wreak havoc on the world’s population’s health and well-being. Successful screening of infected patients is a critical step in the fight against it, with radiology examination using chest radiography being one of the most important screening methods. For the definitive diagnosis of COVID-19 disease, reverse-transcriptase polymerase chain reaction remains the gold standard. Currently available lab tests may not be able to detect all infected individuals; new screening methods are required. We propose a Multi-Input Transfer Learning COVID-Net fuzzy convolutional neural network to detect COVID-19 instances from torso X-ray, motivated by the latter and the open-source efforts in this research area. Furthermore, we use an explainability method to investigate several Convolutional Networks COVID-Net forecasts in an effort to not only gain deeper insights into critical factors associated with COVID-19 instances, but also to aid clinicians in improving screening. We show that using transfer learning and pre-trained models, we can detect it with a high degree of accuracy. Using X-ray images, we chose four neural networks to predict its probability. Finally, in order to achieve better results, we considered various methods to verify the techniques proposed here. As a result, we were able to create a model with an AUC of 1.0 and accuracy, precision, and recall of 0.97. The model was quantized for use in Internet of Things devices and maintained a 0.95 percent accuracy.

Item Type:	Article
Keywords:	4.2 Evaluation of markers and technologies
Schools and Departments:	School of Engineering and Informatics > Informatics
SWORD Depositor:	Mx Elements Account
Depositing User:	Mx Elements Account
Date Deposited:	20 May 2022 07:53
Last Modified:	24 May 2022 08:30
URI:	http://sro.sussex.ac.uk/id/eprint/106008

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