Transfer Learning for Skin Lesion Classification using Convolutional Neural Networks

Recently, deep neural networks achieved state-of-the-art results on the automated diagnosis of skin lesions. Both the availability of bigger and better datasets as well as major advancements in Convolutional Neural Network methodologies represent some of the reasons behind these results. While the former is powered by initiatives like the International Skin Imaging Collaboration (ISIC), the latter is potentiated by developments in CNN architectures and the rise of transfer learning. This paper addresses open research questions related to the effectiveness of transfer learning methods in the context of multi-class skin lesion classification. The results indicate that, depending on the way pre-trained models are re-purposed, recent CNN architectures can bring significant performance boosts on the overall performance of deep learning classifiers. Experiments also highlight the importance of a good dataset to train these models, and how class balancing through data augmentation can help ease this requirement. Furthermore, experimentation with different models shows that ensembles can bring an edge over single-model approaches. Finally, this work presents a competitive single- and multi-model approach to the ISIC 2019 challenge.