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.
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Notes
- 1.
Other clustering methods—such as DBSCAN, OPTICS, SOM—have been tested. k–means was chosen since it offers the best trade off between simplicity and performance.
- 2.
For this reason, the training set dimension has been reduced to balance the number of positive and negative patterns (see Table 2).
- 3.
The MLP structures are described in Table 3. Both hidden and output neurons are sigmoidal. Two neurons constitute the ouput layer in order to improve the network flexibility in modeling complicated relationships. All the architectural parameters (for MLPs and SVMs) were chosen via a trial–and–error procedure and crossvalidation.
- 4.
The Weka Class Balancer function has been used to balance the data. This function reweights the instances in the data so that each class has the same total weight. The total sum of weights across all instances will be maintained. Only the weights in the first batch of data received by this filter are changed.
- 5.
Instead, the codebook generation required about 15 min, using a training set of pre–segmented images.
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Aknowledgements
The authors would like to thank Prof. Rossolini and the whole staff of the MV–Lab of the Careggi Hospital for their willingness to provide real data, and for their invaluable experience in interpreting their microbiological meaning.
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Andreini, P. et al. (2017). Extraction of High Level Visual Features for the Automatic Recognition of UTIs. In: Petrosino, A., Loia, V., Pedrycz, W. (eds) Fuzzy Logic and Soft Computing Applications. WILF 2016. Lecture Notes in Computer Science(), vol 10147. Springer, Cham. https://doi.org/10.1007/978-3-319-52962-2_22
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