COVID-19 Classification based on Neutrosophic Set Transfer Learning Approach

Abstract

The COVID-19 virus has a significant impact on individuals around the globe. The early diagnosis of this infectious disease is critical to preventing its global and local spread. In general, scientists have tested numerous ways and methods to detect people and analyze the virus. Interestingly, one of the methods used for COVID-19 diagnosis is X-rays that recognize whether the person is infected or not. Furthermore, the researchers attempted to use deep learning approaches that yielded quicker and more accurate results. This paper used the ResNet-50 module based on the Neutrosophic (NS) domain to diagnose COVID patients over a balanced database collected from a COVID-19 radiography database. The method is a future work of the N. E. M. Khalifa et al.’s method for NS set significance on deep transfer learning. True (T), False (F), and Indeterminate (I) membership sets were used to define chest X-ray images in the NS domain. Experimental results confirmed that the proposed approach achieved a 98.05% accuracy rate outperforming the accuracy value acquired from previously conducted studies within the same database.

Keywords

• covid-19; chest x-ray; neutrosophic set; resnet-50, classification.