生物组织的吸收系数和散射系数与组织的生理状态相关，是检测人体健康状态的重要指标。当前双层生物组织模型的光学参数反演方案中，吸收系数和散射系数的预测精度受到上层组织厚度等参数的影响较大，限制了模型的实际应用范围。为此，提出一种对上层组织厚度等参数不敏感的吸收和散射系数反演方法。通过采集漫反射光信号的空间和时间分布信息，并利用卷积神经网络来反演双层生物组织的吸收系数和散射系数，在随机的上层组织厚度和折射率等参量下实现较高的吸收系数和散射系数反演精度。在仿真实验中，基于改进的蒙特卡罗模拟获得双层皮肤模型在不同空间探测位置处、不同时刻的漫反射光强信息，然后利用卷积神经网络实现对两层皮肤组织吸收系数和散射系数的预测。结果表明：在固定的上层组织厚度和折射率参数下，吸收系数和散射系数反演的平均相对误差均小于4%；而当上层组织厚度和折射率存在随机变化时，吸收系数和散射系数的平均相对误差仍小于8%。相较其他方法，所提的测量方案和反演算法进一步提升了反演精度和扩展了实际应用场景，为生物组织光学参数的无创测量提供了新思路。

The absorption and scattering coefficients of biological tissue are related to the physiological state of the tissue, which are important parameters for the detection of human health. The prediction accuracy of absorption and scattering coefficients in the current optical property inversion method of the double-layer biological tissue model is greatly affected by parameters such as the thickness of the upper layer tissue, which limits the scope of application. In this study, an inversion method of absorption and scattering coefficients is proposed, which is insensitive to parameters such as the thickness of upper layer tissue, in which the spatial and temporal distribution information of diffuse reflectance is collected; then, convolution neural network algorithm is applied to predict the absorption and scattering coefficients of double-layer biological tissue. The inversion accuracy of absorption and scattering coefficients is high under the random parameters of thickness and refractive index of upper layer tissue. In the simulation experiment, using a modified Monte-Carlo simulation, the diffuse reflectance of the double-layer skin model at different space detection positions and different time is obtained, and the convolution neural network is trained and tested using the simulation data to predict the absorption and scattering coefficients of two-layer skin tissue. Results show that the mean relative errors of absorption and scattering coefficients are less than 4% when the upper layer tissue thickness and refractive index are constant, whereas when the upper layer tissue thickness and refractive index change randomly, the mean relative errors of absorption and scattering coefficients are still less than 8%. Compared with other methods, the measurement scheme and inversion algorithm proposed in this study improve the prediction accuracy and expand the practical application prospect and provide a new method for the noninvasive measurement of biological tissue optical properties.