To be, or not to be Melanoma : Convolutional neural networks in skin lesion classification
Machine learning methods provide an opportunity to improve the classification of skin lesions and the early diagnosis of melanoma by providing decision support for general practitioners. So far most studies have been looking at the creation of features that best indicate melanoma. Representation learning methods such as neural networks have outperformed hand-crafted features in many areas. This work aims to evaluate the performance of convolutional neural networks in relation to earlier machine learning algorithms and expert diagnosis. In this work, convolutional neural networks were trained on datasets of dermoscopy images using weights initialized from a random distribution, a network trained on the ImageNet dataset and a network trained on Dermnet, a skin disease atlas. The ensemble sum prediction of the networks achieved an accuracy of 89.3% with a sensitivity of 77.1% and a specificity of 93.0% when based on the weights learned from the ImageNet dataset and the Dermnet skin disease atlas and trained on non-polarized light dermoscopy images. The results from the different networks trained on little or no prior data confirms the idea that certain features are transferable between different data. Similar classification accuracies to that of the highest scoring network are achieved by expert dermatologists and slightly higher results are achieved by referenced hand-crafted classifiers. The trained networks are found to be comparable to practicing dermatologists and state-of-the-art machine learning methods in binary classification accuracy, benign – melanoma, with only little pre-processing and tuning.
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