Document Type : Research Paper
Author
College of mathematical and computer sciences- Kufa University
Abstract
Gout skin detection and tracking has been the topics of an extensive research for the several
past decades. Many heuristic and pattern recognition based strategies have been proposed for achieving
robust and accurate solution.This paper demonstrates how a Gout skin detection recognition system can be
designed with artificial neural network. Note that the training process did not consist of a single call to a
training 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 propagation
artificial neural network (ANN) model that could distinguish gout image by several parameters for testing
are Energy , Entropy , Average andVariance. Although only the color indices associated with image pixels
were 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 then
cropped to 100x100-pixel images in testing phase. A total of ٨٠ images of gout image and other images
were used for training purposes. For ANNs, the success rate for classifying gout image was as high as
100% .
Keywords
- Back propagation artificial neural network
- Image Processing
- Color space
- Model selection
- Skin Detection
Main Subjects
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