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Urinary tract infection (UTI) is a type of health disorder, an infection in the urinary glands mainly caused by bacteria. Currently, conventional early detection methods that have been established involve rapid dipstick strip test and urine culture analysis, which have suboptimal accuracy and effectiveness. Several retrospective studies regarding UTI bacteria classification have shown promising results, but still have limitations regarding prediction accuracy and technical simplicity. This study aims to implement a method based on artificial intelligence (AI) in classifying images of bacteria that causes UTIs. Eight artificial intelligence methods based on deep neural networks were used in the study; the models were evaluated and compared based on the prediction's effectiveness and accuracy. This study also seeks to create the easiest method of classifying bacteria causing UTIs using a computer-based application with the best obtained AI-based model. The best training results using an intelligent approach placed DenseNet201 as the method with the highest accuracy (83.99%). Then, the output model was used as a knowledge reference for the designed computer-based application. Real-time prediction results will appear in the application window.
Fadlil et al. (Sat,) studied this question.