Detection System of Varicose Disease using Probabilistic Neural Network

Background The diagnoses systemof varicose disease has a good level of performance due to the complexity and uniqueness in patterns of vein of the leg. In addition, the patterns of vein are internal of the body, and its features are hard to duplicate, this reason make this method not easy to fake, and thus make it contains of a good features for varicose disease diagnoses. The proposed system used more than one type of distances with probabilistic neural network (PNN) to produce diagnoses system of varicose disease with high accuracy, in addition, this system based on veins as a factor to recognize varicose infection. Objective of this research is to identify the best available evidence on the diagnosis of varicose veins of the lower limbs. The obtained results indicate that the design of varicose diagnoses system by applying mulit-types of distances (Euclidean and Manhattan) with probabilistic neural network produced new system with high accuracy and low (FAR& FRR) as soon as possible. The results of the proposed systemindicate that the varicose disease detection when using Euclidean distance is better than using Manhattan with probabilistic neural network.