利用深度学习网络对医学影像分类识别的比较研究

Comparative Study on Classification and Recognition of Medical Images Using Deep Learning Network

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。

Computer-aided diagnosis technology has practical significance in clinical medicine.The images of lung nodules and articulatio coxae fractures are used as typical regional and boundary feature images to discuss their applicability in different networks.First,the CT images of the lung nodules and the X-ray fracture images of the articulatio coxae are labeled,and they are pre-trained with CNN,Resnet,DBN and SGAN and fine-tuned,and the classification and recognition are completed via the Softmax classifier.Secondly,the image spatial resolution and noise are used as the comparative characteristics of different deep lear-ning networks,and the recognition rate is analyzed from the aspects of deep learning network structures.The simulation experiment results show that Resnet performs preeminently in all data sets,and has striking generalization ability and robustness.