Abstract
This paper describes combination rule of normal degrees in human body in automated medical diagnosis system. The normal degree is defined in a framework of fuzzy logic. Physician usually examines whether a patient is either normal or abnormal for a disease. The normal degree is calculated in automated medical diagnosis system. The practical examples of medical images and blood test are described. In it, it is shown that union or inter-section operators are introduced for calculating normal degrees on MR meniscal tear images and blood test for diabetes.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Giarratano J, Riley G (1998) Expert Systems: Principles and Programming”, 3rd Edition, PWS Publishing Co pp. 509–556
For example: Computer Assisted Surgery and Medical Image Topics in Trans. of IEEE EMBS Society
Hata Y, Ishikawa O, Kobashi S, Kondo K, (2003) Degree of Normality Based on Fuzzy Logic for a Diagnostic Analysis of Signs Observed in a Human Body, Proc. 20th Annual Meeting of the North American Fuzzy Information Processing Society-NAFIPS, pp. 155–160
Hata Y, Ishikawa O, Kobashi S, Kondo K, (2004) Automated Medical Diagnosis System (AMDS) with Normal Degree Based on Fuzzy Logic, Proc. 2nd IASTED Int. Conf. on Biomedical Engineering, pp. 590–593, Austria
Zadeh LA (2003) Fuzzy Logic as a Basis for a Theory of Hierarchical Definability, Proc. of Int. Sympo. on Multiple-Valued Logic, pp. 3–4
Zadeh LA (2003). A Perception-Based Approach to Decision Analysis and Causality” Proc. 20th Annual Meeting of the North American Fuzzy Information Processing Society-NAFIPS, p. 1
Hata Y, Kobashi S, Tokimoto Y, Ishikawa M (2001) Computer Aided Diagnosis System of Meniscal Tears with T1 and T2 weighted MR Images Based on Fuzzy Inference, 7th FUZZY DAYS Int. Conf. on Computational Intelligence, pp. 55–58
Sasaki T, Hata Y, Ando Y, Ishikawa M (1999) Fuzzy rule-based approach to segment the menisci regions from MR images, Proc. SPIE Medical Imaging 1999, vol. 3661, pp. 258–263
Hata Y, Kobashi S, Hirano S, Kitagaki H, Mori E (2000) Automated Segmentation of Human Brain MR Images Aided by Fuzzy Information Granulation and Fuzzy Inference, IEEE Trans. Syst., Man, Cybern. C, 30:3: 381–395
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hata, Y., Ishikawa, O., Kobashi, S., Kondo, K. (2005). Combination Rule of Normal Degrees on Automated Medical Diagnosis System (AMDS). In: Reusch, B. (eds) Computational Intelligence, Theory and Applications. Advances in Soft Computing, vol 33. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-31182-3_31
Download citation
DOI: https://doi.org/10.1007/3-540-31182-3_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22807-3
Online ISBN: 978-3-540-31182-9
eBook Packages: EngineeringEngineering (R0)