Machine Learning and Deep Learning Methods for Pathogen Identification and Classification of Pathogens Review Article
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Abstract
Machine learning (ML) and deep learning (DL) have become powerful tools in medical sciences, offering rapid, low-cost, and accurate pathogen detection compared to conventional methods. A variety of algorithms, including SVM, NB, RF, and k-NNC, have been widely applied in microbiology for bacterial identification, genetic classification, and image analysis. Beyond bacteria, ML/DL approaches have improved detection of protozoan pathogens by recognizing different life-cycle stages and host pathogen interactions, while also advancing early viral diagnosis, particularly during the COVID-19 pandemic. Foodborne pathogen surveillance has further benefited from the integration of ML with imaging techniques such as hyperspectral analysis. Despite these advances, challenges remain, including data bias, high computational requirements, and the need for better generalization across diverse datasets. This review summarizes current applications of ML/DL in pathogen identification, highlights recent progress, and discusses limitations and future opportunities for improving diagnostic accuracy and disease management.
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