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A Study of the Neutrosophic Set Significance on Deep Transfer Learning Models: An Experimental Case on a Limited COVID-19 Chest X-Ray Dataset

Nour Eldeen M. KhalifaFlorentin SmarandacheMohamed Loey

出版	Infinite Study
主題	Mathematics / Applied
URL	http://books.google.com.hk/books?id=GiL6DwAAQBAJ&hl=&source=gbs_api
EBook	SAMPLE

註釋

Coronavirus, also known as COVID-19, has spread to several countries around the world. It was announced as a pandemic disease by The World Health Organization (WHO) in 2020 for its devastating impact on humans. With the advancements in computer science algorithms, the detection of this type of virus in the early stages is urgently needed for the fast recovery of patients. In this paper, a study of neutrosophic set significance on deep transfer learning models over a limited COVID-19 chest x-ray dataset will be presented. The study relies on neutrosophic set theory, as it shows a huge potential for solving many computers problems related to the detection, and the classification domains.