Melanoma Classification Based on Ensemble Classification of Dermoscopy Image Features
Melanoma Classification Based on Ensemble Classification of Dermoscopy Image Features
复制标题
DOI：
10.1007/978-3-319-13461-1_28
复制
发表时间：
2014-11
期刊：
影响因子：
--
通讯作者：
G. Schaefer;B. Krawczyk;M. E. Celebi;H. Iyatomi;A. Hassanien
中科院分区：
文献类型：
--
作者：
G. Schaefer;B. Krawczyk;M. E. Celebi;H. Iyatomi;A. Hassanien
恶性黑色素瘤是最致命的皮肤癌，也是世界上增长最快的癌症之一。如果早期诊断，它可以很容易地治愈，因此早期诊断黑色素瘤至关重要。在本文中，我们提出了一种有效的方法来黑色素瘤分类皮肤病变的皮肤镜图像。首先，我们执行自动边界检测，以描绘从背景皮肤的病变。然后，我们从边界提取形状特征，而颜色和纹理特征是基于使用欧几里得距离变换将图像划分为临床显著区域而获得的。然后，在模式分类阶段使用派生的功能，我们采用了专门的集成学习方法来解决训练数据中的类不平衡。特别是，我们为此目的雇用了一个单类分类器委员会。单类分类使用来自单个分布的样本来导出决策边界，并且在少数类上采用这种方法可以显着提高其识别率，从而提高我们方法的灵敏度。我们结合联合收割机几个一类分类器使用随机子空间方法和多样性措施，以选择委员会的成员。在皮肤镜皮肤病变图像的大数据集上的实验结果显示，我们的方法工作良好，所采用的分类器选择阶段对于实现这种性能至关重要，并且分类器集合与几个最先进的集合相比在统计上表现得更好。
Malignant melanoma is the deadliest form of skin cancer and is one of the most rapidly increasing cancers in the world. If diagnosed early, it can be easily cured, and consequently early diagnosis of melanoma is of vital importance. In this paper, we present an effective approach to melanoma classification from dermoscopy images of skin lesions. First, we perform automatic border detection to delineate the lesion from the background skin. We then extract shape features from the border, while colour and texture features are obtained based on a division of the image into clinically significant regions using a Euclidean distance transform. The derived features are then used in a pattern classification stage for which we employ a dedicated ensemble learning approach to address the class imbalance in the training data. In particular, we employ a committee of one-class classifiers for that purpose. One-class classification uses samples from a single distribution to derive a decision boundary, and employing this method on the minority class can significantly boost its recognition rate and hence the sensitivity of our approach. We combine several one-class classifiers using a random subspace approach and a diversity measure to select members of the committee. Experimental results on a large dataset of dermoscopic skin lesion images show our approach to work well, the employed classifier selection stage to be crucial for achieving this performance, and the classifier ensembles to perform statistically better compared to several state-of-the-art ensembles.