Deep Learning Based Pathogen Detection System

The timely and accurate identification of pathogenic microorganisms is fundamental to clinical diagnostics, food safety, and environmental monitoring. Conventional methods, primarily based on manual inspection of culture plates followed by biochemical tests, are labour-intensive, time-consuming, and require significant domain expertise, leading to delays in critical interventions. This paper presents a novel automated system that leverages machine learning and computer vision to rapidly identify and quantify bacterial pathogens directly from digital images of petri dish cultures. The system employs a deep learning-based object detection model, specifically a fine-tuned You Only Look Once (YOLO) architecture, to locate, classify, and count bacterial colonies. By training the model on a curated dataset of images featuring different species, the system learns to distinguish pathogens based on morphological characteristics such as colony size, shape, color, and texture. This approach significantly accelerates the analysis process from days to minutes, offering a scalable, consistent, and cost-effective solution to augment the capabilities of modern microbiology laboratories.