Skin Cancer Image Classification with Pre-trained Convolutional Neural Network Architectures

Sammanfattning: In this study we compare the performance of different pre-trained deep convolutional neural network architectures on classification of skin lesion images. We analyse the ISIC skin cancer image dataset. Our results indicate that the architectures analyzed achieve similar performance, with each algorithm reaching a mean five-fold cross-validation ROC AUC value between 0.82 and 0.89. The VGG-11 architecture achieved highest performance, with a mean ROC AUC value of 0.89, despite the fact that it performs considerably worse than some of other architectures on the ILSVRC task. Overall, our results suggest that the choice of architecture may not be as crucial on skin-cancer classification compared with the ImageNet classification problem.