Classification of melanoma images using 2D multifractal detrended cross-correlation analysis

Abstract

Skin cancer is a common human malignant tumor and melanoma is one of the most fatal diseases in skin cancer. There exists a high degree of visual similarity between melanoma and non-melanoma. In addition, the acquisition and labeling of skin cancer images need relevant medical knowledge, therefore, it is difficult to obtain natural images. These problems make it difficult to distinguish between melanoma and non-melanoma. How to extract the high-dimensional features of skin cancer images is the main problem to improve the classification performance of skin cancer images. For this purpose, we propose a new computer-aided classification system with a two-dimensional (2D) multifractal detrended fluctuation analysis (MF-DCCA) method and classifier combination. The proposed 2D MF-DCCA is expanded by the 2D MF-DFA and we aim to change the distribution of the generalized Hurst exponents calculated by the original 2D MF-DFA, making the image features represented by the generalized Hurst exponents calculated by 2D MF-DCCA are more significant. Therefore, we take the generalized Hurst exponents calculated by the two methods into the classifier and evaluate their classification performance. The classification metrics, such as Accuracy (Acc), Sensitivity (Sen), and Specificity (Spe) of two classifiers, show that the features extracted by MF-DCCA are better than those by MF-DFA. In addition, among the two classifiers such as the SVM and k-NN, the k-NN performs best in Acc and Spe with 97.72% and 97.72%, respectively. The k-NN performs better than SVM in the classification Sen with 98.94% +/- 0.1213.