Опубликована: Окт. 14, 2023

In the fight against COVID-19 pandemic, it is crucial to quickly and accurately identify SARS-Co V-2 variants due their ever-changing nature. this study, we introduce a novel approach utilizing Convolutional Neural Networks (CNN) classify spike protein sequences of virus, achieving an outstanding accuracy rate 99.75%. For method, transformed range sequences, representing diverse SARS-CoV-2 variants, into images using Kyte Doolittle method align with CNN input features. Comparative analyses existing methodologies demonstrate superior efficiency our in terms speed precision. Such advancements diagnostics play fundamental role shaping timely informed public health strategies. Our research results showcase potential deep learning tackling global challenges laying groundwork for future innovations virus diagnostics,

Язык: Английский