Melanoma is the most lethal form of skin cancer, but early detection can lead to effective treatment. Subsequently, the main concern of the health management community is to create efficient systems to detect melanoma earlier by utilizing computer vision systems since the traditional screening methods are manual, time-consuming, and inaccurate in some cases. These systems use measurable visual components describing the shape, color, and texture. These features are extracted based on rules invented by dermatologists to determine the malignancy of skin lesions. In this paper, we propose a novel approach to melanoma detection based on the “ugly duckling” concept, which suggests that nevi in the same individual usually resemble each other, and malignant melanomas often do not follow this pattern. Our method uses a convolutional neural network architecture to extract feature vectors from dermatoscopic images of skin lesions. Then, out-liers are detected by applying principal component analysis. The outliers are indicative of potential melanoma lesions. We evaluate the performance of our method using a dataset of dermatoscopic images. Our proposed method has shown the potential to improve melanoma detection rates.

To develop an efficient device that helps dermatologists to early evaluate and inspect a specific kind of skin disease, computer vision systems have been intensively studied. These systems replace the traditional screening ways which are manual and time-consuming. These systems use some measurable visual component describing the shape, color, and texture of skin diseases to recognize them and to specify their malignancy. This article will be concentrated on the importance of using some statistical features and extracting the most relevant features of texture-colored images by calculating their degree of characterization. Using these highly-rated static textural features, non-fatal skin disease and melanoma classification results are presented and discussed.