论文标题
深度转移学习特征和黑色素瘤检测的分类模型
Bucket of deep transfer learning features and classification models for melanoma detection
论文作者
论文摘要
恶性黑色素瘤是皮肤癌最致命的形式，近年来，在全球发病率方面正在迅速增长。靶向治疗的最有效方法是早期诊断。深度学习算法，特别是卷积神经网络，代表了图像分析和表示的方法。他们优化了功能设计任务，对于包括医疗在内的不同类型的图像的自动方法至关重要。在本文中，我们采用了预处理的卷积神经网络体系结构，以预测皮肤病变黑色素瘤的目的。首先，我们应用了转移学习方法来提取图像特征。其次，我们在整体分类上下文中采用了转移的学习功能。具体而言，该框架在平衡子空间上训练单个分类器，并通过统计措施结合了提供的预测。进行了皮肤病变图像数据集的实验阶段，结果获得的结果表明，相对于最先进的竞争者，提出的方法的有效性。
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.
