Abstract
Melanoma is of the lethal and rare types of skin cancer. It is curable at an initial stage and the patient can survive easily. It is very difficult to screen all skin lesion patients due to costly treatment. Clinicians are requiring a correct method for the right treatment for dermoscopic clinical features such as lesion borders, pigment networks, and the color of melanoma. These challenges are required an automated system to classify the clinical features of melanoma and non-melanoma disease. The trained clinicians can overcome the issues such as low contrast, lesions varying in size, color, and the existence of several objects like hair, reflections, air bubbles, and oils on almost all images. Active contour is one of the suitable methods with some drawbacks for the segmentation of irregular shapes. An entropy and morphology-based automated mask selection is proposed for the active contour method. The proposed method can improve the overall segmentation along with the boundary of melanoma images. In this study, features have been extracted to perform the classification on different texture scales like Gray level co-occurrence matrix (GLCM) and Local binary pattern (LBP). When four different moments pull out in six different color spaces like HSV, Lin RGB, YIQ, YCbCr, XYZ, and CIE L*a*b then global information from different colors channels have been combined. Therefore, hybrid fused texture features; such as local, color feature as global, shape features, and Artificial neural network ( ANN) as classifiers have been proposed for the categorization of the malignant and non-malignant. Experimentations had been carried out on datasets Dermis, DermQuest, and PH2. The results of our advanced method showed superiority and contrast with the existing state-of-the-art techniques.