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. 2022 Jul:123:108966.
doi: 10.1016/j.asoc.2022.108966. Epub 2022 May 13.

Explainable artificial intelligence-based edge fuzzy images for COVID-19 detection and identification

Qinhua Hu  1 , Francisco Nauber B Gois  2 , Rafael Costa  3 , Lijuan Zhang  4 , Ling Yin  5 , Naercio Magaia  6   7 , Victor Hugo C de Albuquerque  8   9
Affiliations

Explainable artificial intelligence-based edge fuzzy images for COVID-19 detection and identification

Qinhua Hu et al. Appl Soft Comput. 2022 Jul.

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.

Keywords: COVID-19; Intern of Things; Multi-input convolutional network; Soft computing; X-ray; XAI.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
The second phase of proposed method.
Fig. 2
Fig. 2
Image samples of the chosen datasets.
Fig. 3
Fig. 3
X-ray samples with fuzzy filter.
Fig. 4
Fig. 4
Description of the dataset used in this study.
Fig. 5
Fig. 5
Loss and AUC by epoch results.
Fig. 6
Fig. 6
Loss and AUC by epoch results.
Fig. 7
Fig. 7
Class Activation Map of SARS sample X-ray.
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