Explicable AI for surveillance and interpretation of Coronavirus using X-ray imaging
5th International Conference on Emerging Smart Computing and Informatics, ESCI 2023
; 2023.
Article
in English
| Scopus | ID: covidwho-2322372
ABSTRACT
Explainable AI (XAI) is one of the disciplines being investigated, with the goal of improving the transparency of black-box systems. XAI is such a technology that could assist to alleviate the black-box system by providing new ways of understanding the core thinking process of AI systems. Conside ring the healthcare domain, doctors are still not able to explain why certain decisions or forecasts had been predicted by a particular system. As a result, it imposes limitations on how and where AI technology can be implemented. And to address this problem, a taxonomy of model interpretability is framed for conceptualizing the explainability. Also, an approach with the baseline system is created which could firstly differentiate in the Covid-19 positive and Covid-19 negative chest X-ray images and an automated explainable pipeline is designed using XAI technique. This technique shows that the model is interpretable, that is the achieved results are easy to understand and can encourage medicians and patients with transparent and reliable medical journey. This article aims to help people comprehend the necessity for Explainable AI, as well as the methodological approaches used in healthcare. © 2023 IEEE.
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Prognostic study
Language:
English
Journal:
5th International Conference on Emerging Smart Computing and Informatics, ESCI 2023
Year:
2023
Document Type:
Article
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