Abstract
This paper presents two classification systems for medical images based on association rule mining. The system we propose consists of: a pre-processing phase, a phase for mining the resulted transactional database, and a final phase to organize the resulted association rules in a classification model. The experimental results show that the method performs well, reaching over 80% in accuracy. Moreover, this paper illustrates how important the data cleaning phase is in building an accurate data mining architecture for image classification.
Maria-Luiza Antonie was partially supported by Alberta Ingenuity Fund and Osmar R. Zaïane was partially funded by NSERC, Canada
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Antonie, ML., Zaïane, O.R., Coman, A. (2003). Associative Classifiers for Medical Images. In: Zaïane, O.R., Simoff, S.J., Djeraba, C. (eds) Mining Multimedia and Complex Data. PAKDD 2002. Lecture Notes in Computer Science(), vol 2797. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39666-6_5
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DOI: https://doi.org/10.1007/978-3-540-39666-6_5
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