Classification of Water Microorganisms using Deep Learning

Classification of water microorganisms is essential for maintaining water quality and protecting public health. Microbiological contamination poses serious challenges due to the diverse sizes, shapes, and morphologies of microorganisms, compounded by the inefficiency of traditional methods, which are often costly, labor-intensive, and time-consuming. This study addresses these issues by leveraging deep learning techniques, including Convolutional Neural Networks to accurately identify and classify microorganisms in water samples. Pre-trained models, such as Residual Network 50, Visual Geometry Group 19, and EfficientNet-B0, were integrated with Support Vector Machines to enhance classification performance. The methodology was implemented using Python and tools such as PyTorch, Scikit-learn, and OpenCV, with GPU acceleration enabled through CUDA and cuDNN. Evaluation metrics, including precision, recall, F1-score, and accuracy, demonstrated outstanding performance, with results reaching precision, recall, and accuracy scores of up to 0.98. These findings highlight the effectiveness of advanced deep learning models in addressing the limitations of traditional approaches, offering a rapid, reliable solution for monitoring water quality and safeguarding public health.