Authors : Dr. Arpitha C. N.; Sahana V. S.; Dr. Pushpa Ravikumar; Dr. Anser Pasha C. A.

Volume/Issue : Volume 11 - 2026, Issue 6 - June

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Google Scholar : https://tinyurl.com/4xbxf8fa

Scribd : https://tinyurl.com/3jx9knfa

DOI : https://doi.org/10.38124/ijisrt/26jun1678

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Abstract : This paper presents a two-stage deep learning pipeline for automated classification of urinary tract infection (UTI) pathogens from agar plate images. Stage 1 employs YOLO11m for colony detection on full annotated agar plate images from three public datasets. Stage 2 employs EfficientNetV2-M with a custom classifier head (1280→512→5) identifying five clinically significant UTI pathogens: Escherichia coli, Klebsiella pneumoniae, Proteus mirabilis, Pseudomonas aeruginosa, and Staphylococcus aureus.

Keywords : UTI Pathogen Classification; YOLO11; EfficientNetV2-M; Agar Plate Analysis; Bacterial Colony Detection; CLAHE.

References :

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