APPLICATION OF NEUTROSOPHIC SET TRANSFER LEARNING APPROACH TO CLASSIFY MARBURG-VIRUS

Abstract

Background: This study aimed to People all throughout the Africa are affected significantly by the Marburg-Virus. Preventing and classify the republic of Africa and local spread of this infectious disease requires early detection. Scientists have generally experimented with a wide range of techniques for both person detection and viral analysis.

Methodology: we used a Qualitative Action Research to collect X-rays that identify Marburg-Virus are one of the techniques employed for diagnosis. whether the individual is afflicted. Additionally, in an effort to provide faster and more reliable findings, the researchers tried to apply deep learning techniques.

Materials: This study employed a balanced database gathered from a Marburg-Virus radiography database, along with the Using the Neutrosophic (NS) domain as its foundation, the ResNet-50 module diagnoses Marburg-Virus patients.

Results: According on experimental data, the suggested method outperformed a precision value obtained from 98.25% reliability rate. earlier research projects carried out using the same database.

Conclusion: The technique is a development of the NS significance method of N. E. M. Khalifa et al. give deep transfer learning NS relevance. True (T), False (F), In the NS domain, chest X-ray pictures were defined using and Indeterminate (I) membership sets.