基于RefineNet卷积神经网络对CT颅底骨折精准诊断的应用价值
Application value of RefineNet convolutional neural network in accurate diagnosis of skull base fracture on CT
林煜文 1黄冬云 1龙琳2
作者信息
- 1. 广东省深圳市龙岗区第二人民医院医学影像科 广东 深圳 518112
- 2. 广东省深圳市罗湖区中医院放射科 广东 深圳 518001
- 折叠
摘要
目的 探讨RefineNet卷积神经网络对CT检查颅底骨折精准诊断的应用价值.方法 选取于我院进行头颅CT检查的患者 90 例,其中 46 例表现为颅底骨折(颅底骨折组),44 例则为正常颅脑(对照组).分析患者的特征,明确RefineNet 结构参数;对比分析 Efficient Net,VGG16,Dense Net,Res Net-50 和 Xception 五种模型与 RefineNet的性能差异;分析Mobile Net,Inception-v3,Dense Net-201,CUMED和SDL五种已有方法与RefineNet的性能差异.验证Re-fineNet卷积神经网络与人工测试的召回率、精准率以及测试所需时间.结果 RefineNet的准确率和AUC值显著高于 5种模型和已有的 5 种方法;RefineNet卷积神经网络在骨折患者、颅底区域、前颅区域、中颅区域和后颅区域的召回率和精准率均显著高于人工测试的精准率,差异有统计学意义(P<0.05);RefineNet卷积神经网络所需的测试时间显著低于人工测试所需的时间,差异有统计学意义(P<0.05).结论 RefineNet卷积神经网络对CT检查颅底骨折诊断精准率高、所需时间少.
Abstract
Objective To explore the application value of RefineNet convolutional neural network in the accurate diagnosis of skull base fractures on CT.Methods A total of 90 patients who underwent head CT imaging in our hospital from June 2019 to June 2022 were selected.According to analysis,46 patients were found to have skull base fracture(skull base fracture group in this study),and 44 patients were normal brain(control group in this study).Patient characteristics were analyzed to define RefineNet structural parameters;The performance differences between Efficient Net,VGG16,Dense Net,Res net-50 and Xcep-tion and RefineNet were compared and analyzed.Meanwhile,the performance differences between five existing methods,includ-ing Mobile Net,Inception-v3,Dense NET-201,CUMED and SDL and RefineNet,were analyzed.RefineNet convolutional neu-ral network and manual testing recall,accuracy,and testing time were verified.Results The accuracy and AUC values of Re-fineNet were significantly higher than those of the five models and the five existing methods.The recall and accuracy of RefineNet convolutional neural network in fracture patients,skull base region,anterior cranial region,middle cranial region and posterior cranial region were significantly higher than those of manual test(P<0.05).The testing time required by RefineNet convolutional neural network was significantly less than that required by manual testing(P<0.05).Conclusion RefineNet convolutional neu-ral network has high accuracy and less time required for CT diagnosis of skull base fractures.
关键词
RefineNet卷积神经网络/体层摄影术,X线计算机/颅底骨折Key words
RefineNet convolutional neural network/Tomography,X-ray computed/Skull base fracture引用本文复制引用
基金项目
广东省深圳市龙岗区医疗卫生科技计划项目(LGKCYLWS2019000384)
出版年
2024
NETL
NSTL
万方数据