  Dermatological diseases are among the most common disorders worldwide. This
paper presents the first study of the interpretability and imbalanced
semi-supervised learning of the multiclass intelligent skin diagnosis framework
(ISDL) using 58,457 skin images with 10,857 unlabeled samples. Pseudo-labelled
samples from minority classes have a higher probability at each iteration of
class-rebalancing self-training, thereby promoting the utilization of unlabeled
samples to solve the class imbalance problem. Our ISDL achieved a promising
performance with an accuracy of 0.979, sensitivity of 0.975, specificity of
0.973, macro-F1 score of 0.974 and area under the receiver operating
characteristic curve (AUC) of 0.999 for multi-label skin disease
classification. The Shapley Additive explanation (SHAP) method is combined with
our ISDL to explain how the deep learning model makes predictions. This finding
is consistent with the clinical diagnosis. We also proposed a sampling
distribution optimisation strategy to select pseudo-labelled samples in a more
effective manner using ISDLplus. Furthermore, it has the potential to relieve
the pressure placed on professional doctors, as well as help with practical
issues associated with a shortage of such doctors in rural areas.
