Journal of Computer Science

Psoriasis Detection Using Skin Color and Texture Features

Nidhal K. Al Abbadi, Nizar Saadi Dahir, Muhsi A. AL-Dhalimi and Hind Restom

DOI : 10.3844/jcssp.2010.648.652

Journal of Computer Science

Volume 6, Issue 6

Pages 648-652

Abstract

Problem statement: In this study a skin disease diagnosis system was developed and tested. The system was used for diagnosis of psoriases skin disease. Approach: Present study relied on both skin color and texture features (features derives from the GLCM) to give a better and more efficient recognition accuracy of skin diseases. We used feed forward neural networks to classify input images to be psoriases infected or non psoriasis infected. Results: The system gave very encouraging results during the neural network training and generalization face. Conclusion: The aim of this worked to evaluate the ability of the proposed skin texture recognition algorithm to discriminate between healthy and infected skins and we took the psoriasis disease as example.

Copyright

© 2010 Nidhal K. Al Abbadi, Nizar Saadi Dahir, Muhsi A. AL-Dhalimi and Hind Restom. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.