Explainable AI to Analyze Outcomes of Spike Neural Network in Covid-19 Chest X-rays
2021 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2021
; : 3408-3415, 2021.
Article
in English
| Scopus | ID: covidwho-1705183
ABSTRACT
Analysis of irregularities in Covid-19 data could open a new window to learn more about the unprecedented problems of the current global pandemic. Of many, radiographs and clinical records are reliable sources for viral infection investigation and treatment planning. Clinical records help track the Covid-19 pandemic. In this paper, we present a Spike Neural Network (SNN) with supervised synaptic learning to detect abnormalities in Chest X-rays (CXRs) In other words, the proposed SNN can distinguish Covid-19 positive cases from healthy ones. In our decision-making procedure, we introduce clinical practice so Explainable AI (XAI) is possible to carry out. In addition, Support Vector Machine (SVM) with local interpretable model-agnostic explanation (LIME) provides reliable analysis of abnormalities in Covid-19 clinical data. © 2021 IEEE.
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Database:
Scopus
Language:
English
Journal:
2021 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2021
Year:
2021
Document Type:
Article
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