  This study focuses on automatic skin cancer detection using a Meta-learning
approach for dermoscopic images. The aim of this study is to explore the
benefits of the generalization of the knowledge extracted from non-medical data
in the classification performance of medical data and the impact of the
distribution shift problem within limited data by using a simple class and
distribution balancer algorithm. In this study, a small sample of a combined
dataset from 3 different sources was used to fine-tune a ResNet model
pre-trained on non-medical data. The results show an increase in performance on
detecting melanoma, malignant (skin cancer), and benign moles with the prior
knowledge obtained from images of everyday objects from the ImageNet dataset by
20 points. These findings suggest that features from non-medical images can be
used towards the classification of skin moles and that the distribution of the
data affects the performance of the model.
