Abstract
This paper provides a new classification approach combining different methods for image and text analysis. In this work the approach is applied endoscopic image of gastrointestinal tract and appropriate text reports. We propose to extract useful information about gastrointestinal tract images from text descriptions using semantic analysis. The text mining algorithm was validated on real text descriptions of endoscopic surveys.
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Acknowledgements
The work was done within the framework of the project performed by SIAMS Company, and supported by the Ministry of Education and Science of the Russian Federation (Grant agreement 14.576.21.0018 dated June 27, 2014). Project (applied research) unique ID RFMEFI57614X0018.
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Mizgulin, V.V., Stepanov, D.M., Kamentsev, S.A., Kadushnikov, R.M., Fedorov, E.D., Buntseva, O.A. (2015). Hybrid Classification Approach to Decision Support for Endoscopy in Gastrointestinal Tract. In: Khachay, M., Konstantinova, N., Panchenko, A., Ignatov, D., Labunets, V. (eds) Analysis of Images, Social Networks and Texts. AIST 2015. Communications in Computer and Information Science, vol 542. Springer, Cham. https://doi.org/10.1007/978-3-319-26123-2_21
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DOI: https://doi.org/10.1007/978-3-319-26123-2_21
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