Gout Images Detection and Recognition by Neural Network


Gout skin detection and tracking has been the topics of an extensive research for the severalpast decades. Many heuristic and pattern recognition based strategies have been proposed for achievingrobust and accurate solution.This paper demonstrates how a Gout skin detection recognition system can bedesigned with artificial neural network. Note that the training process did not consist of a single call to atraining function. Instead, the network was trained several times on various input ideal and noisy images,the images which contents Gout skin . The objective of this study was to develop a back propagationartificial neural network (ANN) model that could distinguish gout image by several parameters for testingare Energy , Entropy , Average andVariance. Although only the color indices associated with image pixelswere used as inputs, it was assumed that the ANN model could develop the ability to use other information,such as shapes, implicit in these data. The 756x504 pixel images were taken in the field and were thencropped to 100x100-pixel images in testing phase. A total of ٨٠ images of gout image and other imageswere used for training purposes. For ANNs, the success rate for classifying gout image was as high as100% .