Abstract: Skin recognition is used in several applications ranging from algorithms for face recognition hand gesture analysis, medical image diagnosis and to offensive image filtering. In this effort a skin recognition system was developed for different classes of skin disease images and tested. As many skin segmentation algorithms relay on skin color, our work relies on texture features (features derives from the GLCM) to give a better and more efficient recognition accuracy of skin textures. We used feed forward back propagation neural networks to classify input textures images into three different classes. These classes represent three different skin disease conditions. The system gave very encouraging results during the neural network generalization face.
Keywords: skin recognition, texture analysis, neural networks.
Title: MULTICLASS SKIN DISEASE CLASSIFICATION USING NEURAL NETWORK
Author: Manish Pawar, Proff. Dipesh Kumar Sharma, Proff. R.N. Giri
International Journal of Computer Science and Information Technology Research
ISSN 2348-120X (online), ISSN 2348-1196 (print)
Research Publish Journals
