Epizootic Ulcerative Syndrome (EUS) is a critical disease that afflicts freshwater fish, marked by ulcerative lesions and elevated death rates, hence hurting aquaculture companies worldwide. Timely and precise identification is crucial to halt the dissemination of EUS and mitigate financial damages. Conventional techniques, including eye inspection and histopathological analysis, tend to be laborious, expensive, and may exhibit insufficient sensitivity. Artificial intelligence (AI) methodologies, particularly machine learning and deep learning models, provide effective solutions for swift and precise EUS identification through the analysis of pictures, environmental data, and outbreak-associated patterns. This research investigates the utilization of AI methodologies for the detection of EUS in freshwater fish, emphasizing techniques, model precision, and practical ramifications. Our research indicates that AI enhances EUS detection rates, facilitating more efficient disease control in aquaculture.

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