Skin Diseases Detection using LBP and WLD- An Ensembling Approach
This provides an incremental tool for early skin disease screening in medical imaging, potentially aiding healthcare in developing and developed countries.
The researchers tackled the problem of automatically detecting three skin diseases (Leprosy, Tinea versicolor, Vitiligo) from lesion images using an ensemble of Weber local descriptor and Local binary pattern features, achieving 91.38% accuracy with a multi-level SVM classifier, which outperformed deep learning networks like ResNet_101 at 89% accuracy.
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.