山东大学学报 (工学版) ›› 2022, Vol. 52 ›› Issue (3): 25-33.doi: 10.6040/j.issn.1672-3961.0.2022.024
• • 上一篇
黄彩云1,陈德武2,何吉福1,胡艺1,王楠1,陈沛1
HUANG Caiyun1, CHEN Dewu2, HE Jifu1, HU Yi1, WANG Nan1, CHEN Pei1
摘要： 针对传统的基于专家知识经验医学图像病变检测算法存在稳定性较差、计算复杂度较高、无法适应新出现病例等问题,提出一种基于改进双路径网络(dual path networks with skip connections, SkipConn_DPN)的上肢肌肉骨骼X射线照片异常检测方法。借助深度学习卷积神经网络在图像处理领域的优异性能,整合改进的深度残差网络在图像特征重用和密集连接卷积网络可以不断挖掘新图像特征的优点,并引入可以实现不同尺度图像特征融合的多条跳跃连接。在不增加计算资源消耗的前提下,该方法训练的8个SkipConn_DPN-92网络模型对MURA验证集中不同上肢研究类型的准确率均高于相同参数设置下训练的DPN-92、ResNet-101、ResNeXt-101(32×4d)和DenseNet-169网络模型,分别高约1.64%、2.21%、1.87%和3.22%,并且在临床上可以实现上肢肌肉骨骼异常的实时检测。提出的方法易于实现软件模块,可以作为放射科医生初步诊断的可视化辅助工具,具有良好的应用前景。
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