宿州学院学报2024，Vol.39Issue(12)：24-28,5.DOI:10.3969/j.issn.1673-2006.2024.12.005
基于深度学习ResNet的CTA图像脑动脉瘤自动检测
Deep Learning ResNet-based Cerebral Aneurysm CTA Image Recognition
摘要
Abstract
Cerebral aneurysm is a serious cerebrovascular disease with high mortality and disability rates after rup-ture.The timely detection of early cerebral aneurysms is of great significance for clinical diagnosis and treatment.Computed tomography angiography(CTA)has become a key technology for the diagnosis of cerebral aneurysms.However,traditional manual interpretation methods are not only inefficient,but also prone to misdiagnosis and missed diagnosis due to human factors.To improve the diagnostic efficiency and accuracy of cerebral aneurysms,ResNet network is applied to the automatic detection task of cerebral aneurysms.Three different depths of ResNet networks,ResNet 34,ResNet 50,and ResNet 101,were used to conduct experiments on a self built head CTA image dataset,and compared and analyzed with three classic deep learning methods,MobileNet,AlexNet,and VGG16.The experimental results show that the ResNet based automatic detection model for cerebral aneurysms can exhibit better detection performance than other deep learning methods.关键词
深度学习/卷积神经网络/脑动脉瘤/图像检测Key words
Deep learning/Convolutional neural networks/Cerebral aneurysm/Image detection分类
信息技术与安全科学引用本文复制引用
张雪原,叶明全,陈璇,王家琦,吴爱萍,殷鹏展..基于深度学习ResNet的CTA图像脑动脉瘤自动检测[J].宿州学院学报,2024,39(12):24-28,5.基金项目
安徽省重点研究与开发计划项目(2022a05020011) （2022a05020011）
安徽省高校协同创新项目(GXXT-2022-044) （GXXT-2022-044）
安徽省高校优秀科研创新团队项目(2022AH010075) （2022AH010075）
安徽省高校学科(专业)拔尖人才学术资助项目(gxbjZD2022042). （专业）
