  Skin cancer is one of the major types of cancers with an increasing incidence
over the past decades. Accurately diagnosing skin lesions to discriminate
between benign and malignant skin lesions is crucial to ensure appropriate
patient treatment. While there are many computerised methods for skin lesion
classification, convolutional neural networks (CNNs) have been shown to be
superior over classical methods. In this work, we propose a fully automatic
computerised method for skin lesion classification which employs optimised deep
features from a number of well-established CNNs and from different abstraction
levels. We use three pre-trained deep models, namely AlexNet, VGG16 and
ResNet-18, as deep feature generators. The extracted features then are used to
train support vector machine classifiers. In the final stage, the classifier
outputs are fused to obtain a classification. Evaluated on the 150 validation
images from the ISIC 2017 classification challenge, the proposed method is
shown to achieve very good classification performance, yielding an area under
receiver operating characteristic curve of 83.83% for melanoma classification
and of 97.55% for seborrheic keratosis classification.
