Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, Kraków, Poland.
PLoS One. 2019 Feb 6;14(2):e0211318. doi: 10.1371/journal.pone.0211318. eCollection 2019.
This article refers to the Computer Aided Diagnosis of the melanoma skin cancer. We derive wavelet-based features of melanoma from the dermoscopic images of pigmental skin lesions and apply binary C-SVM classifiers to discriminate malignant melanoma from dysplastic nevus. The aim of this research is to select the most efficient model of the SVM classifier for various image resolutions and to search for the best resolution-invariant wavelet bases. We show AUC as a function of the wavelet number and SVM kernels optimized by the Bayesian search for two independent data sets. Our results are compatible with the previous experiments to discriminate melanoma in dermoscopy images with ensembling and feed-forward neural networks.
这篇文章涉及黑色素瘤皮肤癌的计算机辅助诊断。我们从色素性皮肤病变的皮肤镜图像中提取基于小波的黑色素瘤特征，并应用二进制 C-SVM 分类器将恶性黑色素瘤与发育不良的痣区分开来。本研究的目的是为各种图像分辨率选择最有效的 SVM 分类器模型，并寻找最佳的分辨率不变小波基。我们展示了 AUC 作为小波数和 SVM 核函数的函数，这些核函数是通过贝叶斯搜索针对两个独立数据集进行优化的。我们的结果与以前使用集成和前馈神经网络在皮肤镜图像中区分黑色素瘤的实验结果相兼容。
