Advancing real-time Viral strains identification and microbiological diagnostics with YOLOv8 and Machine learning

Abstract

Combining virology with machine learning (ML) might completely transform the diagnosis of infectious diseases, particularly when it comes to precisely identifying viruses at the microscopic level. In an effort to greatly improve the speed and accuracy of microbiological diagnostics, this work presents an advanced ML framework that uses the YOLO (You Only Look Once) architecture for real-time viral identification. By comparing three top deep learning models such as convolutional neural network (CNN) and the latest YOLOv8. The study exhibits YOLOv8's superior performance, attaining a stunning validation accuracy of 75.4% over 100 epochs. This noteworthy advancement highlights YOLOv8's potential to deliver more rapid more precise diagnoses, which is essential for prompt action in infectious disease pandemics. The model's real-time capabilities underline the broader implications of machine learning in advancing global public health policies and redefining treatment paradigms, making it a crucial instrument for high-throughput viral screening. Our research represents an important advance in the field of microbiology and opens the door for the creation of scalable and effective diagnostic methods in the ongoing battle against infectious illnesses.