COVIDQuantization1.pdf (2.57 MB)
Explainable artificial intelligence-based edge fuzzy images for COVID-19 detection and identification
journal contribution
posted on 2023-06-10, 03:37 authored by Qinhua Hu, Francisco Nauber B Gois, Rafael Costa, Lijuan Zhang, Ling Yin, Naercio Magaia, Victor Hugo C de AlbuquerqueThe 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.
History
Publication status
- Published
File Version
- Accepted version
Journal
Applied Soft ComputingISSN
1568-4946Publisher
ElsevierExternal DOI
Volume
123Page range
1-19Article number
a108966Department affiliated with
- Informatics Publications
Full text available
- Yes
Peer reviewed?
- Yes
Legacy Posted Date
2022-05-20First Open Access (FOA) Date
2023-05-14First Compliant Deposit (FCD) Date
2022-05-24Usage metrics
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