Psoriasis detection using skin color and texture features

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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. © 2010 Science Publications.

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APA

El Abbadi, N., Dahir, N. S., AL-Dhalimi, M. A., & Restom, H. (2010). Psoriasis detection using skin color and texture features. Journal of Computer Science, 6(6), 648–652. https://doi.org/10.3844/jcssp.2010.648.652

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