Identification of Skin Diseases Based on Blind Chromophore Separation and Artificial Intelligence

We propose in this paper a new approach for identifying skin diseases from RGB dermatological images. Based on Blind Source Separation and Artificial Intelligence (AI), this approach proceeds in two steps. We begin by estimating the concentrations of the three main skin Chromophore separately, adopting a new source separation technique that exploits both their spatial sparsity and their positivity. We then utilize these concentrations to extract the more relevant features for classification in our second step using AI, rather than those directly extracted from the three spectral bands of the image, as used by most existing methods. The results of tests performed on two different databases of RGB dermatological images of melanoma and nevus demonstrate the superiority of our approach, in terms of melanoma identification, compared to two types of existing methods based on AI.