PDF(Pubmed)
Abstract：
The study aimed to achieve the following objectives: (1) to perform the fusion of thermal and visible tongue images with various fusion rules of discrete wavelet transform (DWT) to classify diabetes and normal subjects; (2) to obtain the statistical features in the required region of interest from the tongue image before and after fusion; (3) to distinguish the healthy and diabetes using fused tongue images based on deep and machine learning algorithms. The study participants comprised of 80 normal subjects and age- and sex-matched 80 diabetes patients. The biochemical tests such as fasting glucose, postprandial, Hba1c are taken for all the participants. The visible and thermal tongue images are acquired using digital single lens reference camera and thermal infrared cameras, respectively. The digital and thermal tongue images are fused based on the wavelet transform method. Then Gray level co-occurrence matrix features are extracted individually from the visible, thermal, and fused tongue images. The machine learning classifiers and deep learning networks such as VGG16 and ResNet50 was used to classify the normal and diabetes mellitus. Image quality metrics are implemented to compare the classifiers\' performance before and after fusion. Support vector machine outperformed the machine learning classifiers, well after fusion with an accuracy of 88.12% compared to before the fusion process (Thermal-84.37%; Visible-63.1%). VGG16 produced the classification accuracy of 94.37% after fusion and attained 90.62% and 85% before fusion of individual thermal and visible tongue images, respectively. Therefore, this study results indicates that fused tongue images might be used as a non-contact elemental tool for pre-screening type II diabetes mellitus.
摘要：
该研究旨在实现以下目的：（1）使用离散小波变换（DWT）的各种融合规则对热和可见舌头图像进行融合，以对糖尿病和正常受试者进行分类；（2）从融合前后的舌头图像中获取所需感兴趣区域的统计特征；（3）使用基于深度和机器学习算法的融合舌头图像区分健康和糖尿病。研究参与者包括80名正常受试者和年龄和性别匹配的80名糖尿病患者。生化测试，如空腹血糖，餐后,所有参与者都服用Hba1c。使用数字单镜头参考相机和热红外摄像机获取可见和热舌头图像，分别。基于小波变换的方法对数字和热舌图像进行融合。然后分别从可见的图像中提取灰度共生矩阵特征,热,和融合的舌头图像。机器学习分类器和深度学习网络（如VGG16和ResNet50）用于对正常和糖尿病进行分类。实施图像质量度量以比较融合前后的分类器性能。支持向量机优于机器学习分类器，与融合前相比，融合后的准确度为88.12%(热-84.37%;可见-63.1%)。VGG16在融合后产生了94.37%的分类准确率，在融合个体热和可见舌头图像之前达到了90.62%和85%。分别。因此,这项研究结果表明，融合的舌象可以作为一种非接触的基础工具，用于预先筛查II型糖尿病。
