论文标题
扩展特征基于空间的自动黑色素瘤检测系统
Extended Feature Space-Based Automatic Melanoma Detection System
论文作者
论文摘要
黑色素瘤是皮肤癌最致命的形式。黑色素细胞的不可控制的生长导致黑色素瘤。在过去的几十年中，黑色素瘤一直在疯狂增长。近年来，使用图像处理技术对黑色素瘤的检测已成为主要的研究领域。自动黑色素瘤检测系统（AMDS）通过接受感染的皮肤面积图像作为输入来帮助基于图像处理技术检测黑色素瘤。单个病变图像是多个特征的来源。因此，至关重要的是从病变图像中选择适当的特征，以提高AMD的准确性。对于黑色素瘤检测，所有提取的特征并不重要。一些提取的功能很复杂，需要更多的计算任务，这会影响AMDS的分类准确性。 AMD的特征提取阶段表现出更大的可变性，因此，使用个体和扩展的特征提取方法研究AMD的行为很重要。提出了一种新型算法ExtfVAMDS来计算扩展特征矢量空间。比较研究中提出的六个模型表明，HSV具有用于使用Med-Node数据集上的集合包装的树分类器自动检测黑色素瘤的矢量空间，可提供99％的AUC，95.30％的准确性，94.23％的敏感性和96.96％的特异性。
Melanoma is the deadliest form of skin cancer. Uncontrollable growth of melanocytes leads to melanoma. Melanoma has been growing wildly in the last few decades. In recent years, the detection of melanoma using image processing techniques has become a dominant research field. The Automatic Melanoma Detection System (AMDS) helps to detect melanoma based on image processing techniques by accepting infected skin area images as input. A single lesion image is a source of multiple features. Therefore, It is crucial to select the appropriate features from the image of the lesion in order to increase the accuracy of AMDS. For melanoma detection, all extracted features are not important. Some of the extracted features are complex and require more computation tasks, which impacts the classification accuracy of AMDS. The feature extraction phase of AMDS exhibits more variability, therefore it is important to study the behaviour of AMDS using individual and extended feature extraction approaches. A novel algorithm ExtFvAMDS is proposed for the calculation of Extended Feature Vector Space. The six models proposed in the comparative study revealed that the HSV feature vector space for automatic detection of melanoma using Ensemble Bagged Tree classifier on Med-Node Dataset provided 99% AUC, 95.30% accuracy, 94.23% sensitivity, and 96.96% specificity.
