Skin Diseases Classification Using Local Binary Pattern and Convolutional Neural Network

Abstract

Skin disease is one of the diseases that are often found in tropical countries like Indonesia. Lack of knowledge about the types and prevention of skin diseases results a person suffering from acute skin diseases. Computer technology is expected to help detect disease early so that it can minimize the occurrence of more dangerous diseases. This paper proposes a method for introducing the shape, color, and texture of skin diseases in digital images and classifying the results of image analysis based on the type of disease in human skin. The method used is a combination of Local Binary Pattern (LBP) and Convolutional Neural Network (CNN) methods which can later be used as sensors or vision for skin diseases automatically. The results of this study can help in the early identification of skin diseases, helping parties who want to know the image value of skin diseases by using LBP and classifying it based on the type of disease using CNN. This study shows the level of accuracy of combining LBP with CNN is quite high with an average value of 92%. In addition, this research can also be used as reference material for the development of further research in image processing that uses LBP and classification using CNN.