Hand Image Based Skin Disease Identification Using Machine Learning and Deep Learning Algorithms

Abstract

The skin disease is most commonly affected to all peoples. It is a big challenging process in skin image identification because of the combined texture shape with the color variations. This paper presented to identify a skin using texture features (Local Binary Pattern and Gray Level Co-occurance Matrix) and color features with various learning models. The proposed work technique for identification of skin disease has two stages: either extracted feature values and identification. During the first stage, texture features and color feature vector values are taken from the images in a dataset of hand skin database. During the second stage, the extracted feature vectors are trained by the various kinds of Machine Learning models. This hand skin image dataset also applied into Deep Learning model for identification. The performance measures of the system is evaluated using accuracy measure. The Convolutional Neural Network (CNN) model gives accuracy of 87.5% when compared to Machine Learning models.