AI For Detecting Waterborne Diseases through Image Analysis

Abstract

The detection of waterborne pathogens is essential for safeguarding public health, especially in regions with limited access to clean and safe water. Traditional methods of microorganism detection often rely on time-consuming and resource-intensive laboratory techniques. To address this, the present study introduces an artificial intelligence-based solution using the YOLOv5 deep learning algorithm to detect and classify microorganisms in water samples. The model is trained on the Environmental Microorganism Dataset (EMDS-7), which comprises a comprehensive collection of labelled images representing 41 distinct microorganism types. YOLOv5 is chosen for its high-speed real-time object detection capabilities and accuracy. This approach significantly reduces the reliance on manual processes, enabling faster, automated, and scalable pathogen detection. Experimental results demonstrate a high level of precision, indicating the model's effectiveness in real-world scenarios. The proposed system holds strong potential for public health surveillance, particularly in underserved or remote areas where rapid diagnosis and response are critical to preventing the spread of waterborne diseases.