A Soft Computing Based Approach Using Signal-To-Image Conversion for Computer Aided Medical Diagnosis (CAMD)
Dealing with expert (human) knowledge consideration, Computer Aided Medical Diagnosis (CAMD) dilemma is one of the most interesting, but also one of the most difficult problems. Among difficulties contributing to challenging nature of this problem, one can mention the need of fine classification and decision tasks. In this paper, we present a new approach founded on an hybrid scheme, multiple model approach for reliable CAMD, including a signal-to-image converter, a Neural Network (NN) based classifier and a fuzzy decider. This new concept has been used to design a computer aided medical diagnostic tool able to assert auditory pathologies based on Brainstem Auditory Evoked Potentials (BAEP) based biomedical tests, which provides an effective measure of the integrity of the auditory pathway.
KeywordsComputer Aided Medical Diagnosis (CAMD) image processing analysis and interpretation of biomedical signals pattern recognition classification neural networks fuzzy decision-making
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