Malignant melanoma is the deadliest form of skin cancer and, in recent years, is rapidly growing in terms of the incidence worldwide rate. The most effective approach to targeted treatment is early diagnosis. Deep learning algorithms, specifically convolutional neural networks, represent a methodology for the image analysis and representation. They optimize the features design task, essential for an automatic approach on different types of images, including medical. In this paper, we adopted pretrained deep convolutional neural networks architectures for the image representation with purpose to predict skin lesion melanoma. Firstly, we applied a transfer learning approach to extract image features. Secondly, we adopted the transferred learning features inside an ensemble classification context. Specifically, the framework trains individual classifiers on balanced subspaces and combines the provided predictions through statistical measures. Experimental phase on datasets of skin lesion images is performed and results obtained show the effectiveness of the proposed approach with respect to state-of-the-art competitors.

中文翻译：

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用于黑色素瘤检测的深度迁移学习特征和分类模型

恶性黑色素瘤是最致命的皮肤癌形式，近年来，就全球发病率而言，它正在迅速增长。靶向治疗最有效的方法是早期诊断。深度学习算法，特别是卷积神经网络，代表了一种图像分析和表示的方法。他们优化了特征设计任务，这对于不同类型图像（包括医学）的自动方法至关重要。在本文中，我们采用预训练的深度卷积神经网络架构进行图像表示，目的是预测皮肤病变黑色素瘤。首先，我们应用迁移学习方法来提取图像特征。其次，我们在集成分类上下文中采用了迁移学习特征。具体来说，该框架在平衡的子空间上训练单个分类器，并通过统计测量组合提供的预测。执行皮肤病变图像数据集的实验阶段，获得的结果表明所提出的方法相对于最先进的竞争对手的有效性。