Design and validation of a new machine-learning-based diagnostic tool for the differentiation of dermatoscopic skin cancer images.

We classified seven distinct skin lesions in the HAM10000 dataset with an EfficientNet model reaching an accuracy of 84.3%, which provides a promising outlook for further development of more accurate models.