Vitiligo disease prediction using K-mean, GLCM and voting classification

Vitiligo is a disorder in the cells that generate the major pigment, and melanin of skin. Vitiligo vulgaris is an autoimmune disease which is developed unpredictably. There may be the occurrence of vitiligo in any area of the body and in any shape. It sometimes even covers the whole surface of the skin. Vitiligo can be seen clearly on an individual having dark-phototype and leads to cause socio-relational issues. The ultraviolet light is utilized to eliminate the issue of visibility disparity. The vitiligo disease diagnosis consists of various processes: pre-processing, feature extraction, segmentation and classification. The technique of vitiligo disease is proposed in which K-mean, GLCM and voting ensemble are applied for segmenting, feature extracting, classifying the disorder. The voting ensemble is applied as a blend of logistic regression, random forest and GNB (Gaussian Naive Bayes) methods. The framework constructed in this project is enforced in python software, and based on some metrics the obtained outcomes are studied.