Image Recognition Based Analysis and Comparison of Hybrid Intelligent Approaches for Computer Aided Diagnosis

In this paper, two hybrid intelligent approaches are suggested for computer aided diagnosis systems in a biomedicine application: auditory diagnosis based on auditory brainstem response test. Indeed, these approaches are developed through the hybrid intelligent system 1 (HIS_1) and the hybrid intelligent system (HIS_2), based on neural classification and fuzzy decision-making of global image and subdivided image. In fact, in each system, a double classification is exploited in a primary diagnosis fuzzy system to ensure a certain degree of reliability. This reliability is reinforced using a confidence parameter with the primary diagnosis results (from primary diagnosis fuzzy system), exploited in a final diagnosis fuzzy system, in order to generate the appropriate diagnosis with a confidence index (CI). Afterwards, experimental set up and auditory diagnosis results are presented for HIS_1 and HIS_2, analyzed and compared based on image recognition.