计算机科学 ›› 2021, Vol. 48 ›› Issue (6A): 89-94.doi: 10.11896/jsjkx.201000116
刘汉卿1, 康晓东1, 李博2, 张华丽1, 冯继超1, 韩俊玲1
LIU Han-qing1, KANG Xiao-dong1, LI Bo2, ZHANG Hua-li1, FENG Ji-chao1, HAN Jun-ling1
摘要： 计算机辅助诊断技术在临床医学中具有实际意义。分别以肺结节和髋关节骨折影像为典型的区域和边界特征影像,讨论其在不同网络中的适用性。首先,对肺结节CT图像和髋关节X-ray骨折图像进行信息标注,并分别以CNN,Resnet,DBN和SGAN预训练并调参至最优,通过Softmax分类器完成分类识别。其次,以图像空间分辨率和噪声作为不同深度学习网络的比较特征,从深度学习网络结构等方面分析了识别率。仿真实验结果表明,Resnet在数据集皆有优异表现,且具有良好的泛化能力和鲁棒性。
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