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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

Authors: Nour Eldeen M. Khalifa, Florentin Smarandache, Mohamed Loey

To test the performance of the conversion to the neutrosophic domain, 36 experimental trails have been conducted and presented. A combination of training and testing strategies have been applied into dataset by splitting it to (90%-10%, 80%-20%, and 70%-30%) accordingly. According to the experimental results, the Indeterminacy (I) neutrosophic domain achieves the highest accuracy possible in the testing accuracy and performance metrics such as Precision, Recall, and F1 Score. The study concludes that using the neutrosophic set with deep learning models may be an encouraging transition to achieve higher testing accuracy, especially with limited datasets such as COVID-19 chest x-ray dataset which is investigated throughout this study.

Comments: 17 Pages.

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Submission history

[v1] 2022-12-09 22:06:47

Unique-IP document downloads: 187 times

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