Abstract. Urinary Tract Infections (UTIs) are a severe public health problem, accounting for more than eight million visits to health care providers each year. High recurrence rates and increasing antimicrobial resistance among uropathogens threaten to greatly increase the economic burden of these infections. Normally, UTIs are diagnosed by traditional methods, based on cultivation of bacteria on Petri dishes, followed by a visual evaluation by human experts. The need of achieving faster and more accurate results, in order to set a targeted and sudden therapy, motivates the design of an automatic solution in place of the standard procedure. In this paper, we propose an algorithm that combines a “bag–of–words” approach with machine learning techniques to recognize infected plates and provide the automatic classification of the bacterial species. Preliminary experimental results are promising and motivate the introduction of a visual word dictionary with respect to using low level visual features.
