Toward determination of venous thrombosis ages by using fuzzy logic and supervised Bayes classification

Venous thrombosis is a common pathology that creates serious problems in public health. The diagnosis of thromboses, particularly the determination of their relative ages can be efficiently accomplished by ultrasound imaging. This study intends to classify automatically the thrombosis ages by using a predefined learning base that depends on a prior knowledge of physicians. In practice, this learning base is affected by information imperfections of the type ambiguity since physicians cannot give exact thrombosis ages. Thus, the proposed learning base is constructed in a 3-tuple: observation, label, membership value in term of fuzzy logic for each class and not a 2-tuple as in the usual supervised Bayes classification application. By considering this "fuzzy learning base", a method modeling simultaneously the concept of probabilistic uncertainty and ambiguity is proposed. In this approach, the probability for a given observation is considered on the membership value of each class and not on the class itself. At this level, the discussion focuses on two types of applications: the thrombosis age classification and the definition of membership function by using a fuzzy learning base for classification.