YOLO-Based Bacteria Detection and Identification: A Deep Learning Approach for Microbial Diagnosis

This article describes the rapid detection and identification of bacteria using deep learning. Rapid and accurate detection and identification of bacterial strains is important not only for effective diagnosis and treatment in clinical settings, but also for public health surveillance and ensuring food safety. Traditional bacterial detection methods, some of which, such as culture-based techniques, are time- consuming and labor-intensive, limiting their applicability in high- throughput and real-time analysis. Machine learning algorithms using deep convolutional neural networks (CNNs) offer a promising alternative. Here, we present a deep learning-based approach combined with the Yolo algorithm for rapid and accurate detection of bacterial class identities. Our method is designed to analyze high-dimensional data in real time, achieving rapid identification with high accuracy. We used multi-class classification to identify Gram-positive and Gram- negative strains and differentiate between all tested bacterial strains. We propose a simple YOLO and CNN architecture and use a many- class bacterial isolation dataset for training and testing. We achieve a discrimination accuracy of about 86% with near real-time discrimination speed. Our results show that the processing time is significantly reduced compared to traditional methods, reducing the detection time to minutes. The proposed system provides a scalable solution that can be integrated into clinical and laboratory workflows and will be a valuable tool to improve patient outcomes and public health responses.