  In all developing and developed countries in the world, skin diseases are
becoming a very frequent health problem for the humans of all age groups. Skin
problems affect mental health, develop addiction to alcohol and drugs and
sometimes causes social isolation. Considering the importance, we propose an
automatic technique to detect three popular skin diseases- Leprosy, Tinea
versicolor and Vitiligofrom the images of skin lesions. The proposed technique
involves Weber local descriptor and Local binary pattern to represent texture
pattern of the affected skin regions. This ensemble technique achieved 91.38%
accuracy using multi-level support vector machine classifier, where features
are extracted from different regions that are based on center of gravity. We
have also applied some popular deep learn-ing networks such as MobileNet,
ResNet_152, GoogLeNet,DenseNet_121, and ResNet_101. We get 89% accuracy using
ResNet_101. The ensemble approach clearly outperform all of the used deep
learning networks. This imaging tool will be useful for early skin disease
screening.
