A Soft Computing Based Approach Using Signal-To-Image Conversion for Computer Aided Medical Diagnosis (CAMD)

Amarger

et al. 2014

Vietnam J Comput Sci

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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 first. Then, from a fault diagnosis system analysis of the classification and decision-making problem, a global diagnosis synopsis is deduced. Afterwards, a hybrid intelligent diagnosis approach, based on soft computing implying modular neural networks for classification and fuzzy logic for decision-making, is suggested from signal and image representations. The suggested approach is developed in biomedicine for a CAD, from Auditory Brainstem Response test, and the prototype design and experimental results are presented. In fact, a double classification is exploited in a primary fuzzy diagnosis, to ensure a satisfactory reliability. Then, this reliability is reinforced using a confidence parameter with the primary diagnosis result, exploited in a final fuzzy diagnosis giving the appropriate diagnosis with a Indeed, experimental results demonstrate the efficiency and reliability of CAD for three classes: two auditory pathologies Retro-cochlear Class (RC) and Endocochlear Class (EC), and Normal auditory Class (NC). The generalization rate of NC is clearly higher for primary fuzzy diagnosis and final fuzzy diagnosis than that of the two classifications. The obtained rates for RC and EC are higher than obtained by image classification but quite similar than those obtained by signal classification. An important contribution of the final fuzzy diagnosis is the fact that a confidence index is associated with each fault diagnosis. Finally, a discussion is given with regard to the reliability and large application field of the suggested approach.

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Section: Extraction Of the Two Knowledge Representations (Signal And mentioning

confidence: 99%

Section: Suggested Hybrid Intelligent Diagnosis Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hybrid intelligent diagnosis approach based on soft computing from signal and image knowledge representations for a biomedical application

Amarger

et al. 2014

Vietnam J Comput Sci

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“…Recently, several decision support systems and intelligent systems have been developed [1], [2], [3] and the diagnosis approaches based on such intelligent systems have been developed for industrial applications [1], [4], [5], and biomedicine applications [6], [7], [8], [9], [10], [11], [12], [13]. Currently, one of the most used approaches to feature identification, classification, and decision-making problems inherent to fault detection and diagnosis, is multiple model approaches with soft computing implying mainly neural networks and fuzzy logic [1], [3], [4], [5], [6], [9], [10], [11], [12], [13], [14], [15].…”

Section: Introductionmentioning

confidence: 99%

“…Currently, one of the most used approaches to feature identification, classification, and decision-making problems inherent to fault detection and diagnosis, is multiple model approaches with soft computing implying mainly neural networks and fuzzy logic [1], [3], [4], [5], [6], [9], [10], [11], [12], [13], [14], [15].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Intelligent Diagnosis Systems

6th International Conference on Computer Information Systems and Industrial Management Applications (CISIM'07)

Madani

2007

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In this paper, the main objective is to give a methodology to design hybrid intelligent diagnosis systems for a large field of biomedicine and industrial applications. At first, a brief description on diagnosis tasks in such applications is presented. Second, diagnosis systems are presented. Third, the main steps of hybrid intelligent diagnosis systems are developed, for each step emphasizing problems and suggesting solutions able to ensure the design of hybrid intelligent diagnosis systems with a satisfactory reliability degree. In fact, the main steps discussed are knowledge representation, classification, classifier issued information fusion, and decision-making. Finally, a discussion is given with regard to the suggested methodology.

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Signal and Image Representations Based Hybrid Intelligent Diagnosis Approach for a Biomedicine Application

Advances in Applied Artificial Intelligence

Amarger

et al. 2006