Abstract
Fault diagnosis is a complex and fuzzy cognitive process, and soft computing methods as neural networks and fuzzy logic, have shown great potential in the development of decision support systems. Dealing with expert (human) knowledge consideration, Computer Aided Diagnosis (CAD) 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-making. In this paper, a brief survey on fault diagnosis systems is given. From the classification and decision-making problem analysis, a hybrid intelligent diagnosis approach is suggested from signal and image representations. Then, the suggested approach is developed in biomedicine for a CAD, from Auditory Brainstem Response (ABR) test, and the prototype design and experimental results are presented. Finally, a discussion is given with regard to the reliability and large application field of the suggested approach.
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Chohra, A., Kanaoui, N., Amarger, V., Madani, K. (2006). Signal and Image Representations Based Hybrid Intelligent Diagnosis Approach for a Biomedicine Application. In: Ali, M., Dapoigny, R. (eds) Advances in Applied Artificial Intelligence. IEA/AIE 2006. Lecture Notes in Computer Science(), vol 4031. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11779568_19
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DOI: https://doi.org/10.1007/11779568_19
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