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Knowledge‐based Diagnosis Aiding in Regulation Thermography
Author(s) -
Knaf Hagen,
Lang Patrick
Publication year - 2003
Publication title -
pamm
Language(s) - English
Resource type - Journals
ISSN - 1617-7061
DOI - 10.1002/pamm.200310200
Subject(s) - thermography , expert system , fuzzy logic , linear discriminant analysis , artificial intelligence , fuzzy inference , computer science , process (computing) , fuzzy inference system , medical diagnosis , skin temperature , machine learning , data mining , pattern recognition (psychology) , fuzzy control system , adaptive neuro fuzzy inference system , engineering , biomedical engineering , medicine , infrared , radiology , physics , optics , operating system
Regulation Thermography is a diagnostic tool in the medical science based on the measurement of the body's thermoregulation ability – the so‐called thermogram. The expert's rules for the interpretation of a thermogram can be modelled using Fuzzy Logic. In the present article this modelling process is briefly explained; it leads to a Fuzzy Inference System capable of evaluating thermograms with respect to e.g. signals for the presence of Breast Cancer. Some of the main points of a comparison between the expert rules and the result of a stepwise linear discriminant analysis performed on classified thermograms are presented.