Dermatology diagnosis with feature selection methods and artificial neural network

Dermatology or skin disease is one of the popular diseases among other diseases these days. The features similarities between different types of skin diseases make diagnosis of skin diseases very complex. A patient needs dermatologist that has a sound and vast good experience in skin diseases in order to give precise results at the right time. This paper elaborates a prototype with back propagation neural network (BPNN) to assist the dermatologist. This prototype improves expert diagnosis method in term of time efficiency and diagnosis accuracy. The use of two feature selection methods namely Correlation Feature Selection (CFS) and Fast Correlation-based Filter (FCBF) help by providing a smaller number of features with greater accuracy and faster response time. The adjustment of parameter in BPNN gives good performance. The findings show that FCBF method offers the shortest elapsed time and highest result compared to CFS method and the full features with an accuracy of 91.2%.