【摘要】 针对黑色素瘤分类识别任务中存在对比度低、肉眼难以区分、信息干扰大、数据量偏少以及数据不均衡等诸多问题,文中提出了一种基于掩盖的数据增强与深度卷积残差网络相结合的集成分类方法。首先根据皮肤病图像的特点,在前人数据增强研究的基础上,提出了两种基于掩盖训练图像部分区域的数据增强方式;其次以这两种数据增强方式为基础,采用深度卷积残差50层(ResNet-50)网络进行特征提取;然后以提取到的特征来构建两个具有一定差异性的分类结构模型,并对其进行集成;最后以国际皮肤影像协作组织(ISIC)2016挑战赛所公布的皮肤病图像数据集为对象,通过一系列实验对提出的方法进行了验证测试。实验结果表明,所提出的集成分类结构模型能弥补单一卷积残差网络在黑色素瘤分类任务中的缺陷,该模型能够在训练样本较少的皮肤病数据集上取得较好的分类结果,多项评估指标均优于ISIC2016挑战赛的前5名。更多还原

【Abstract】 To solve the classification problems of melanoma,such as low contrast,indistinguishable by the naked eyes,mass information interference,small dataset and data imbalance,this paper proposed an integrated classification method based on mask data augment and deep convolutional residual network.Firstly,according to the characteristics of skin lesion image and the previous researches,two data augmentation methods by masking the partial area of the training ima-ges were proposed.Secondly,on the basis of these two data augmentation methods,some features were extracted by using deep convolutional residual 50-layer network.Thirdly,two different classification models were constructed and integrated based on these features.Finally,a series of experiments were conducted based on the datasets of Internal Skin Imaging Collaboration(ISIC) 2016 Challenge competition.The experimental results show that the integrated classification structure model can overcome the deficiencies of a single convolution residual network in melanoma classification tasks,and can achieve better classification results than other methods on skin lesion dataset with less training examples,and multiple evaluation indicators in the proposed method are better than the top-5 results in the ISIC2016 Challenge competition.更多还原