Artificial Intelligence Based Skin Classification Using GMM

Abstract

This study describes the usage of neural community based on the texture evaluation of pores and skin a variety of similarities in their signs, inclusive of Measles (rubella), German measles (rubella), and Chickenpox etc. In fashionable, these illnesses have similarities in sample of infection and symptoms along with redness and rash. Various skin problems have similar symptoms. For example, in German measles (rubella), Chicken pox and Measles (rubella) a similarity can be observed in skin rashes and redness. The prognosis of skin problems take a long time as the patient's previous medical records, physical examination report and the respective laboratory diagnostic reports have to be studied. The recognition and diagnosis get tough due to the complexity involved. Subsequently, a computer aided analysis and recognition gadget would be handy in such cases. Computer algorithm steps include image processing, picture characteristic extraction and categorize facts with the help of a classifier with Artificial Neural Network (ANN). The ANN can analyze the patterns of symptoms of a particular disease and present faster prognosis and reputation than a human doctor. For this reason, the patients can undergo the treatment for the pores and skin problems based totally on the symptoms detected.

Keywords: Dominant rotated local binary pattern (DRLBP); Gaussian mixture model classifier (GMM); Gray level co-occurrence matrix (GLCM); Pre-processing; Probabilistic neural network (PNN) classification; Super pixel segmentation.