Almaraz-Damian Jose-Agustin, Ponomaryov Volodymyr, Sadovnychiy Sergiy, Castillejos-Fernandez Heydy
Instituto Politecnico Nacional, Santa Ana Ave. # 1000, Mexico City 04430, Mexico.
Instituto Mexicano del Petroleo, Lazaro Cardenas Ave. # 152, Mexico City 07730, Mexico.
Entropy (Basel). 2020 Apr 23;22(4):484. doi: 10.3390/e22040484.
In this paper, a new Computer-Aided Detection (CAD) system for the detection and classification of dangerous skin lesions (melanoma type) is presented, through a fusion of handcraft features related to the medical algorithm ABCD rule (Asymmetry Borders-Colors-Dermatoscopic Structures) and deep learning features employing Mutual Information (MI) measurements. The steps of a CAD system can be summarized as preprocessing, feature extraction, feature fusion, and classification. During the preprocessing step, a lesion image is enhanced, filtered, and segmented, with the aim to obtain the Region of Interest (ROI); in the next step, the feature extraction is performed. Handcraft features such as shape, color, and texture are used as the representation of the ABCD rule, and deep learning features are extracted using a Convolutional Neural Network (CNN) architecture, which is pre-trained on Imagenet (an ILSVRC Imagenet task). MI measurement is used as a fusion rule, gathering the most important information from both types of features. Finally, at the Classification step, several methods are employed such as Linear Regression (LR), Support Vector Machines (SVMs), and Relevant Vector Machines (RVMs). The designed framework was tested using the ISIC 2018 public dataset. The proposed framework appears to demonstrate an improved performance in comparison with other state-of-the-art methods in terms of the accuracy, specificity, and sensibility obtained in the training and test stages. Additionally, we propose and justify a novel procedure that should be used in adjusting the evaluation metrics for imbalanced datasets that are common for different kinds of skin lesions.
本文提出了一种用于检测和分类危险皮肤病变（黑色素瘤类型）的新型计算机辅助检测（CAD）系统，该系统通过融合与医学算法ABCD规则（不对称性-边界-颜色-皮肤镜结构）相关的手工特征和采用互信息（MI）测量的深度学习特征来实现。CAD系统的步骤可概括为预处理、特征提取、特征融合和分类。在预处理步骤中，对病变图像进行增强、滤波和分割，目的是获得感兴趣区域（ROI）；在下一步中，进行特征提取。形状、颜色和纹理等手工特征用作ABCD规则的表示，深度学习特征则使用在ImageNet（ILSVRC ImageNet任务）上预训练的卷积神经网络（CNN）架构进行提取。MI测量用作融合规则，从两种类型的特征中收集最重要的信息。最后，在分类步骤中，采用了几种方法，如线性回归（LR）、支持向量机（SVM）和相关向量机（RVM）。使用ISIC 2018公共数据集对设计的框架进行了测试。与其他现有方法相比，所提出的框架在训练和测试阶段获得的准确性、特异性和敏感性方面表现出了改进的性能。此外，我们提出并论证了一种新颖的程序，该程序应用于调整不同类型皮肤病变常见的不平衡数据集的评估指标。
