Ugly Duckling Concept for Melanoma Detection: A PCA-Based Outlier Detection Method with CNN-Based Feature Vectors

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.