Evaluation of deep learning approaches for dermoscopic evaluation of vascular features of port-wine stain birthmarks

Dermoscopy is a useful tool for observing the vascular profile of port-wine stain (PWS) birthmarks. However, due to the complicity of the vascular profile, there is a lack of consensus on the classification of dermoscopic features of PWS vessels. This study investigated the potentials of deep learning-assisted methods in the classification of dermoscopy image-based of PWS vascular profiles. The classified images were used as training samples, and the RegNet network with better classification effect was selected to establish the migration learning method. The results showed that the accuracy of the RegNet network on the validation set was 82.63%. The preliminary study suggests that deep learning assisted PWS vascular contour type classification is feasible.