论文标题
基于TCM眼睛诊断中改进的U2NET的一种新的眼睛分割方法
A new eye segmentation method based on improved U2Net in TCM eye diagnosis
论文作者
论文摘要
为了诊断中药，舌分段达到了一个相当成熟的点，但是在中药的眼睛诊断中几乎没有应用。首先，这次，我们根据U2NET网络的体系结构提出RES-UNET，并使用基于降低噪声的小型型号的小型数据集的数据集基于数据集的数据集，并使用数据增强工具范围，该特征是降低噪声的特征。作为评估指标以评估模型。同时，使用MIOU，精度，召回，F1得分和Flops比较了不同的眼睛数据分割框架。为了说服人们，我们引用了Ubivis。 V1公共数据集这次，其中MIOU达到97.8％，S量达到97.7％，F1得分达到99.09％，对于320*320*320 RGB输入图像，总参数量为167.83 MB，由于参数的小量量，我们的参数超过了。在相关指标中，与U2NET相似，该指标验证了我们的结构的有效性。在所有比较网络中，它实现了最佳的分割效果，并为应用后续视觉设备识别症状的应用奠定了基础。
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.
