Addressing Complexity and Variability Issues of SERS Spectra of Clinical Nasopharyngeal Swab (CNS) Samples for Respiratory Viruses Detection using Machine Learning
A machine learning-integrated SERS system is developed that addresses complexity and variability issues of SERS spectra of CNS and enables rapid classification of respiratory viruses with a remarkable detection accuracy of 97.92%, reshaping future diagnostics.
Authors
- Arti Yadav (ORCID: https://orcid.org/0000-0003-3089-3420)
- Partha Pratim Roy (ORCID: https://orcid.org/0000-0002-5735-5254)
- Sachin Kumar Srivastava (ORCID: https://orcid.org/0000-0003-1711-1551)
- Ekta Gupta
- Rakesh Naik
Publication Details
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1364/cleo_at.2025.aa109_3
- Primary Topic
- COVID-19 diagnosis using AI
- Type
- article
- Field-Weighted Citation Impact
- 0.00