A Systematic Literature Review on Machine Learning Techniques for Skin Disease Classification

Abstract

Skin diseases are health problems that require accurate diagnosis to evaluation and ultimately leading to treatment decisions. One of the crucial roles in the diagnostic process is medical imaging. Machine learning technology can assist in classifying skin diseases using image data and achieving high levels of accuracy in diagnosis. The purpose of this research is to review machine learning algorithms that can be utilized to develop image-based skin disease classification systems. The methodology employed is a Systematic Literature Review (SLR), which can be used to provide a comprehensive review of the application of machine learning in the classification of skin diseases. The literature search strategy was based on the Boolean technique, applied to the Scopus database. The selected articles were screened using predefined inclusion and exclusion criteria. The results indicate that the most used machine learning algorithm with achieved the highest classification accuracy is the Convolutional Neural Network (CNN). Keywords - Skin Disease, Machine Learning, Classification, CNN.