Skin Melanoma Classification System Using Deep Learning

Abstract

The deadliest type of skin cancer is malignant melanoma. The diagnosis requires at the earliest to reduce the mortality rate. In this study, an efficient Skin Melanoma Classification (SMC) system is presented using dermoscopic images as a non-invasive procedure. The SMC systemconsists of four modules; segmentation, feature extraction, feature reduction and finally classification. In the first module, k-means clustering is applied to cluster the colour informationof dermoscopic images. The second module extractsmeaningful and useful descriptors based on the statistics of local property, parameters of Generalized Autoregressive ConditionalHeteroscedasticity (GARCH) model of wavelet and spatial patterns by DominantRotated Local Binary Pattern (DRLBP). The third module reduces the features by the t-test, and the last module uses deep learning for the classification. The individual performance shows that GARCH parameters of 3rd DWT level sub-bands provide 92.50% accuracy than local properties (77.5%) and DRLBP (88%) based features for the 1st stage (normal/abnormal). For the 2nd stage (benign/malignant), it is 95.83% (GRACH), 90% (DRLBP) and 80.8% (Local Properties). The selected 2% of features from the combination gives 99.5% and 100% for 1st and 2nd stage of the SMC system. The greatest degree of success is achieved on PH2 database images using two stages of deep learning. It can be used as a pre-screening tool as it provides 100% accuracy for melanoma cases.