Hybrid intelligent diagnosis approaches: Analysis and comparison under a biomedicine application

Computer Aided Diagnosis (CAD) is one of the most interesting and most difficult dilemma dealing on one hand with expert (human) knowledge consideration. On the other hand, fault diagnosis is a complex and fuzzy cognitive process and multiple model approaches with soft computing approaches as modular neural networks and fuzzy logic, have shown great potential in the development of decision support. 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, two hybrid intelligent diagnosis approaches are suggested based on image representation. Then, the suggested approaches are applied, analyzed, and compared in biomedicine for 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 approaches.