Extraction of High Level Visual Features for the Automatic Recognition of UTIs
| Autor: | Francesco Guerri, Andrea Baghini, Simone Bonechi, Giovanni Bianchi, Monica Bianchini, Angelo Galano, Paolo Andreini, Guendalina Vaggelli, Alessandro Mecocci |
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| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
020205 medical informatics Computer science 02 engineering and technology Machine learning computer.software_genre Theoretical Computer Science Standard procedure 03 medical and health sciences Health care 0202 electrical engineering electronic engineering information engineering Urinoculture screening Visual Word Set (psychology) Artificial neural networks Bag-of–words Clustering techniques Color image processing Support vector machines Computer Science (all) Artificial neural network business.industry Support vector machine 030104 developmental biology Bag-of-words model in computer vision Bag-of-words model Artificial intelligence business computer |
| Zdroj: | Fuzzy Logic and Soft Computing Applications ISBN: 9783319529615 WILF |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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