Computational Intelligence and Healthcare Informatics

15A Computational Intelligence Approach for Skin Disease Identification Using Machine/Deep Learning Algorithms

Swathi Jamjala Narayanan*, Pranav Raj Jaiswal, Ariyan Chowdhury, Amitha Maria Joseph and Saurabh Ambar

School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

Abstract

Skin diseases are debilitating to a person’s health and social life. Among skin diseases, the diagnosis of the most common epidermal diseases would be based on the morphological manifestations of the disease. The morphological manifestations play a key role as an indicator for disease diagnosis. Proper image capture of these symptoms is the first step to diagnosis via the remote process. These images need to undergo pre-processing, and then, the necessary features of the images need to be extracted. This data is then subsequently fed into a model implementation of a machine learning algorithm, which has to be trained to detect and label the skin disease. For this purpose, various algorithms have been implemented over the past decade in the detection of diseases that affect the human epidermis. These algorithms have been noted to achieve varying degrees of success with different epidermal diseases. The most commonly used techniques are Random Forest, Decision Trees, and Naïve Bayes, whereas in recent years, deep learning plays a major role to develop accurate models for skin disease detection. In this chapter, we compare the efficacy of different machine ...