To solve the problems of redundant feature information,the insignificant difference in feature representation,and low recognition accuracy of the fine-grained image,based on the ResNeXt50 model,an MSFResNet network mo...To solve the problems of redundant feature information,the insignificant difference in feature representation,and low recognition accuracy of the fine-grained image,based on the ResNeXt50 model,an MSFResNet network model is proposed by fusing multi-scale feature information.Firstly,a multi-scale feature extraction module is designed to obtain multi-scale information on feature images by using different scales of convolution kernels.Meanwhile,the channel attention mechanism is used to increase the global information acquisition of the network.Secondly,the feature images processed by the multi-scale feature extraction module are fused with the deep feature images through short links to guide the full learning of the network,thus reducing the loss of texture details of the deep network feature images,and improving network generalization ability and recognition accuracy.Finally,the validity of the MSFResNet model is verified using public datasets and applied to wild mushroom identification.Experimental results show that compared with ResNeXt50 network model,the accuracy of the MSFResNet model is improved by 6.01%on the FGVC-Aircraft common dataset.It achieves 99.13%classification accuracy on the wild mushroom dataset,which is 0.47%higher than ResNeXt50.Furthermore,the experimental results of the thermal map show that the MSFResNet model significantly reduces the interference of background information,making the network focus on the location of the main body of wild mushroom,which can effectively improve the accuracy of wild mushroom identification.展开更多
目的:构建儿童青少年近视与肥胖共病综合干预实施性研究结局指标,为儿童青少年多病共防及实施性研究结局测量提供依据。方法:基于RE-AIM框架初步构建指标集,采用德尔菲法(Delphi method),通过函询问卷对初步制定的指标集进行评分和建议...目的:构建儿童青少年近视与肥胖共病综合干预实施性研究结局指标,为儿童青少年多病共防及实施性研究结局测量提供依据。方法:基于RE-AIM框架初步构建指标集,采用德尔菲法(Delphi method),通过函询问卷对初步制定的指标集进行评分和建议征询,每一轮专家咨询结束后计算专家积极指数、专家权威程度、专家意见协调程度和专家意见集中程度,整理专家意见,结合专家咨询结果和筛选标准修改、删除或新增指标,并进行下一轮专家咨询,经过两轮专家咨询,意见一致后形成最终指标集。结果:两轮专家咨询实际纳入专家28名,Kendall协调系数W分别为0.352(χ^(2)=413.952,P<0.001)和0.499(χ^(2)=405.044,P<0.001),经检验差异均具有统计学意义,结果可取。最终构建的儿童青少年近视与肥胖共病综合干预实施性研究结局指标包含人群覆盖、干预效果、机构采纳、干预实施和效果维持5个(维度)一级指标下13条二级指标和20条三级指标。人群覆盖维度包括参与项目儿童青少年人数、项目参与者代表性和项目全程参与者代表性;干预效果维度包括共病新发率、近视新发率、等效球镜度数、体重指数(body mass index,BMI)、超重肥胖患病率、腰围身高比、综合健康知识水平得分和综合健康行为水平得分;机构采纳维度包括参与项目学校的代表性和参与项目执行的校医、教师代表性;干预实施维度包括保真度、内容调整和成本;效果维持维度包括个体健康结局和机构持续采用。结论:构建了基于RE-AIM框架的儿童青少年近视与肥胖共病综合干预实施性研究结局指标,可以为中国儿童青少年常见多病干预策略优化提供借鉴。展开更多
基金supported by National Natural Science Foundation of China(No.61862037)Lanzhou Jiaotong University Tianyou Innovation Team Project(No.TY202002)。
文摘To solve the problems of redundant feature information,the insignificant difference in feature representation,and low recognition accuracy of the fine-grained image,based on the ResNeXt50 model,an MSFResNet network model is proposed by fusing multi-scale feature information.Firstly,a multi-scale feature extraction module is designed to obtain multi-scale information on feature images by using different scales of convolution kernels.Meanwhile,the channel attention mechanism is used to increase the global information acquisition of the network.Secondly,the feature images processed by the multi-scale feature extraction module are fused with the deep feature images through short links to guide the full learning of the network,thus reducing the loss of texture details of the deep network feature images,and improving network generalization ability and recognition accuracy.Finally,the validity of the MSFResNet model is verified using public datasets and applied to wild mushroom identification.Experimental results show that compared with ResNeXt50 network model,the accuracy of the MSFResNet model is improved by 6.01%on the FGVC-Aircraft common dataset.It achieves 99.13%classification accuracy on the wild mushroom dataset,which is 0.47%higher than ResNeXt50.Furthermore,the experimental results of the thermal map show that the MSFResNet model significantly reduces the interference of background information,making the network focus on the location of the main body of wild mushroom,which can effectively improve the accuracy of wild mushroom identification.
文摘目的:构建儿童青少年近视与肥胖共病综合干预实施性研究结局指标,为儿童青少年多病共防及实施性研究结局测量提供依据。方法:基于RE-AIM框架初步构建指标集,采用德尔菲法(Delphi method),通过函询问卷对初步制定的指标集进行评分和建议征询,每一轮专家咨询结束后计算专家积极指数、专家权威程度、专家意见协调程度和专家意见集中程度,整理专家意见,结合专家咨询结果和筛选标准修改、删除或新增指标,并进行下一轮专家咨询,经过两轮专家咨询,意见一致后形成最终指标集。结果:两轮专家咨询实际纳入专家28名,Kendall协调系数W分别为0.352(χ^(2)=413.952,P<0.001)和0.499(χ^(2)=405.044,P<0.001),经检验差异均具有统计学意义,结果可取。最终构建的儿童青少年近视与肥胖共病综合干预实施性研究结局指标包含人群覆盖、干预效果、机构采纳、干预实施和效果维持5个(维度)一级指标下13条二级指标和20条三级指标。人群覆盖维度包括参与项目儿童青少年人数、项目参与者代表性和项目全程参与者代表性;干预效果维度包括共病新发率、近视新发率、等效球镜度数、体重指数(body mass index,BMI)、超重肥胖患病率、腰围身高比、综合健康知识水平得分和综合健康行为水平得分;机构采纳维度包括参与项目学校的代表性和参与项目执行的校医、教师代表性;干预实施维度包括保真度、内容调整和成本;效果维持维度包括个体健康结局和机构持续采用。结论:构建了基于RE-AIM框架的儿童青少年近视与肥胖共病综合干预实施性研究结局指标,可以为中国儿童青少年常见多病干预策略优化提供借鉴。
