基于改进深度回归网络的无创血糖检测算法研究

Research on Nondestructive Blood Glucose Cloud Detection System Based on Improved Deep Regression Network

有创测量血糖有强烈不适感和感染风险,所以无创血糖的研究有很强的现实意义。目前光学方法不便于实际使用,能量守恒方法要求严苛,针对以上问题,采用红外线热像图来进行血糖的检测。采集人脸的红外线热像图后,提取其灰度特征再降维,为了加快训练速度和防止过拟合改进了深度回归网络,采用改进的深度回归网络对得到的红外线热像图灰度特征进行建模,在测试集上取得了比较理想的检测效果,为以后的无创血糖检测算法的研究提供了一种新的研究方法和设计思路。

Invasive blood glucose measurement has a strong sense of discomfort and risk of infection, so the study of non-invasive blood glucose has a strong practical significance. At present, the optical method is not convenient for practical use, and the energy conservation method requires strict requirements. In view of the above problems, infrared thermography is used to detect blood glucose. After acquiring infrared thermal images of face figure, we extract the gray feature and reduce its dimension. In order to speed up the training and prevent over fitting, depth regression network is improved to model the infrared thermal image gray feature, and the ideal testing results have been achieved in the test set, which provides a new method of research and design for the noninvasive blood glucose detection algorithm research.