用倒谱纹理特征提升皮肤癌分类准确率
Cepstrum-Based Texture Features for Melanoma Detection
- 将灰度共生矩阵应用于二维倒谱图提取纹理特征
- 在ISIC 2019数据集上提升AUC、准确率和F1分数
- 适合医学图像分析与皮肤癌辅助诊断研究者
本文提出一组基于倒谱的纹理特征，用于黑素瘤分类，并在ISIC 2019数据集的皮肤镜图像上验证其性能。首次将灰度共生矩阵（GLCM）统计量应用于二维倒谱表示，构建新特征。结合传统手工设计的病灶描述符，使用XGBoost模型进行评估。引入部分倒谱特征后，二分类（黑素瘤 vs. 良性痣）的受试者工作特征曲线下面积（AUC）、准确率和F1得分均有所提升。结果表明，倒谱GLCM特征能为黑素瘤检测提供互补的判别信息。
原文摘要 · Abstract (English)
This paper introduces a set of cepstrum-based texture features for melanoma classification and validates their performance on dermoscopic images from the ISIC 2019 dataset. We propose applying gray-level co-occurrence matrix (GLCM) statistics to 2D cepstral representations, a novel approach in image analysis. Combined with established handcrafted lesion descriptors, these features were evaluated using XGBoost models. Incorporating select cepstral features improved the area under the receiver operating characteristic curve, accuracy, and F1 score for binary melanoma vs. nevus classification. Results suggest that cepstral GLCM features offer complementary discriminatory information for melanoma detection.
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