Melanoma Classification based on Simulated Annealing Optimization in Neural Network

Abstract

Technology development in image processing and artificial intelligence field leads to the high demand for smart systems, especially in the health sector. Cancer is one of the diseases that has the highest mortality cases around the world. Melanoma is one of the cancer types that appear caused by high exposure to UV light. The earliest the melanoma was identified, the higher the chance the patient can be recovered. Therefore, this study carries the melanoma detection based on BPNN optimized by a simulated annealing algorithm. This research utilizes PH2 dermoscopic image data which contains 200 color digital images in BMP format. The data is processed using color feature extraction techniques to identify the characteristics of each image according to the target data. The color space extraction used includes mean RGB, HSV, CIE LAB, YCbCr, and XYZ. The evaluation result showed that the BPNN-SA method was able to increase the accuracy performance in classifying skin cancer when compared to the original BPNN method with an overall average accuracy of 84.03%.