Abstract
Nowadays, the pressure on prevent invasive methods for diagnostics is still increasing in the health care sector. In the case of the lower urinary tract, early diagnosis can play a significant role to prevent a surgery. Here, the widely used non-invasive test, the uroflowmetry, is observed. As the new measurement devices are being created, new algorithms for basic urinary flow classification must be developed. There, the feature extraction methods are developed and introduced for further use in combination with standard classifiers based on machine learning. In the further work, the methods will be reviewed on extensive dataset, which is currently being created. As the credible dataset verified by several urologist will be obtained, the proposed methods should be examined. Direction of further development will depend on the results of introduced methods.
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The work has been supported by the IGA Funds of the University of Pardubice, Czech Republic. This support is very gratefully acknowledged.
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Stursa, D., Dolezel, P., Honc, D. (2021). Basic Urinal Flow Curves Classification with Proposed Solutions. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. IntelliSys 2020. Advances in Intelligent Systems and Computing, vol 1250. Springer, Cham. https://doi.org/10.1007/978-3-030-55180-3_56
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DOI: https://doi.org/10.1007/978-3-030-55180-3_56
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