For the diagnosis of Chinese medicine, tongue segmentation has reached a fairly mature point, but it has little application in the eye diagnosis of Chinese medicine.First, this time we propose Res-UNet based on the architecture of the U2Net network, and use the Data Enhancement Toolkit based on small datasets, Finally, the feature blocks after noise reduction are fused with the high-level features.Finally, the number of network parameters and inference time are used as evaluation indicators to evaluate the model. At the same time, different eye data segmentation frames were compared using Miou, Precision, Recall, F1-Score and FLOPS. To convince people, we cite the UBIVIS. V1 public dataset this time, in which Miou reaches 97.8%, S-measure reaches 97.7%, F1-Score reaches 99.09% and for 320*320 RGB input images, the total parameter volume is 167.83 MB,Due to the excessive number of parameters, we experimented with a small-scale U2Net combined with a Res module with a parameter volume of 4.63 MB, which is similar to U2Net in related indicators, which verifies the effectiveness of our structure.which achieves the best segmentation effect in all the comparison networks and lays a foundation for the application of subsequent visual apparatus recognition symptoms.
翻译:对于中国医学的诊断,舌分解已达到相当成熟的点,但它在中国医学的眼诊断中几乎没有应用什么应用。 首先,我们提出基于U2Net网络架构的Res-UNet,并使用基于小数据集的数据增强工具包。 最后,噪音减少后的特性块与高级特征结合。 最后,网络参数和推算时间的数量被用作评价模型的评价指标。与此同时,使用米乌、精密、回召、F1-Score和FLOPS对不同的眼分解框架进行了比较。为了说服人们,我们引用UBIVIS。 V1公共数据集这个时候,米乌达到97.8%,S度达到97.7%,F1-Score达到99.09 %,320RGB输入图像达到320*320,总参数卷为167.83 MB,与过多参数相比,我们用一个小型的U2Net与Res模块和4.63MB的参数数量进行了实验。我们引用了U2Net的参数模块。 V1公共数据集这个时候,MB相当于我们随后的图像结构的对比。