Melanoma Classification based on Simulated Annealing Optimization Neural Network - Kusuma, Edi Jaya; Pantiawati, Ika; Handayani, Sri (Knowledge Engineering and Data Science, 2021)
Technology development in image processing and artificial intelligence leads to the high demand for smart systems, especially in the health sector. Cancer is one of the diseases with the highest mortality cases worldwide. Melanoma is one of the cancers commonly caused by high exposure to UV light. The earliest the melanoma is identified, the higher the patient's chance of recovering. Therefore, this study proposes melanoma detection based on BPNN optimized by a simulated annealing algorithm. This research utilizes PH2 dermoscopic image data containing 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 includes mean RGB, HSV, CIE LAB, YCbCr, and XYZ. The evaluation result showed that the BPNN-SA increased the performance accuracy in classifying skin cancer compared to the original BPNN, with an overall average accuracy of 84.03%.