Feature Extraction in Dermoscopic Images for Analysing Dryness Using Second Order GLCM Techniques

Skin is the primary vital largest organ of Human Body. It is seen than the majority of the people in the world doesn’t experience a good skin life due to various factors and causes. Dermatology is expected to have a startling global market of 8.7 billion dollars in 2022 as it grows daily. It is anticipated to reach approximately $30 billion in value between 2023 and 2028. Skin-related problems are to be expected following COVID-19 and the periodic changes in our lifestyle. In general, genetic or hereditary factors can be used to explain this problem. However, lifestyle factors may also play a role. Skin conditions can affect newborns, young children, teenagers, adults, the elderly, and others. In-depth analysis of the underlying cause and solutions is provided in "Artificial Intelligence in the field of Dermatology." Prediction, diagnosis, and treatment are made more effective when a wide range of solid information is readily available. Artificial Intelligence and technology find a great place in the field of dermatology to comprehend, forecast, and create a wonderful future. Lack of additional data or information for machine learning processing is one of the problems. Despite the field's depth, there is room for improvement in terms of using technology to benefit patients. These are the main elements of challenges; the topic of this essay is skin dryness, which is a prevalent issue. Eczema, dermatitis, skin tumors, and itchiness are all associated with a higher likelihood of dry skin. The suggested method uses Second Order GLCM techniques to identify and examine the skin's textures in dermoscopic images. The improved performance of the classification model will be aided by the feature metrics that were obtained.