融合多注意力的舌象证候分类  

Tongue Image Syndrome Classification Integrated with Multiple Attention

作　　者：宁宏宇 张魁星[1] 薛丹 江梅[1] NING Hong-Yu;ZHANG Kui-Xing;XUE Dan;JIANG Mei(School of Intelligence and Information Engineering,Shandong University of Traditional Chinese Medicine,Jinan 250355,China;The First Affiliated Hospital of Shandong First Medical University(Shandong Provincial Qianfoshan Hospital),Jinan 250013,China)

机构地区：[1]山东中医药大学智能与信息工程学院,济南250355 [2]山东第一医科大学第一附属医院(山东省千佛山医院),济南250013

出　　处：《计算机系统应用》2024年第10期228-235,共8页Computer Systems & Applications

基　　金：山东省自然科学基金(ZR2020KF013);山东省中医药科技项目(Q-2022052);青岛市科技惠民示范专项(23-2-8-smjk-2-nsh)。

摘　　要：智能舌诊在协助医生诊断病情方面具有重要意义.当前,智能舌诊主要集中在单一舌象特征的预测分类,难以在诊断过程中提供实质性的帮助.为弥补这一不足,从舌象证候层面进行精准的预测分类研究,协助医生诊断病情.使用TUNet对舌体进行分割,并提出融合多注意力机制的平行残差网络PMANet用于舌象证候分类.在像素准确率、平均交并比和Dice系数3个评价指标上,TUNet分别达到99.7%、98.4%、99.2%,相较于基线U-Net,提高了3.2%、9.0%、4.8%.在舌象证候分类研究中,PMANet的参数总量为12.34M,略高于对比实验中的EfficientNet,总浮点计算数为1.021G,远低于所有对比网络.在参数量和浮点计算数更少的情况下,取得了95.7%的分类准确率,实现了精度、参数量和浮点运算数之间的平衡.这一方法为智能舌诊研究提供了重要支持,有望推进中医舌诊现代化进程.Intelligent tongue diagnosis is of great significance in assisting doctors in medical treatment.At present,intelligent tongue diagnosis is mainly focused on the prediction and classification of single tongue image features,making it difficult to provide substantial help in the diagnostic process.To make up for this deficiency,research of accurate prediction and classification is carried out from the level of tongue image syndrome to assist doctors in diagnosing diseases.The TUNet is used to segment the tongue,and a parallel residual network PMANet integrated with the multiattention mechanism is proposed to classify the syndrome of tongue image.the pixel accuracy(PA),mean intersection over union(MIoU)and Dice coefficient of TUNet reach 99.7%,98.4%,and 99.2%,respectively,improved by 3.2%,9.0%,and 4.8%compared with the baseline U-Net.In the research of tongue image syndrome classification,PMA’s total amount of parameters is 12.34M,slightly higher than that of EfficientNet,and its total amount of floating-point calculations is 1.021G,significantly lower than all compared networks.Under the background of a lower amount of both parameters and floating-point calculations,the classification accuracy of PMANet reaches 95.7%,achieving a balance between precision,parameter amount,and floating-point calculations amount.This method provides support for the research of intelligent tongue diagnosis and is expected to promote the modernization of TCM tongue diagnosis.

关 键 词：舌诊 舌体分割 舌象证候分类 深度学习 舌诊智能化 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TP391.41[自动化与计算机技术—控制科学与工程] R241[医药卫生—中医诊断学]