基于双模态深度自编码的孤立性肺结节诊断方法

Pulmonary Nodule Diagnosis Using Dual-modal Denoising Autoencoder Based on Extreme Learning Machine

近年来,深度学习技术在肺癌诊断方面得到了广泛的应用,但现有的研究主要集中于肺部CT图像。为了有效提高肺结节的诊断性能,提出一种基于双模态深度降噪自编码的肺结节诊断方法。首先,分别从肺部CT和PET图像中得到肺结节区域的特征信息;然后,以候选结节的PET/CT图像作为整个深度自编码网络的输入,并对高层信息进行学习;最后,采用融合策略对多种特征进行融合并将其作为整个框架的输出。实验结果表明,提出的方法可以达到92.81%的准确率、91.75%的敏感度和1.58%的特异性,且优于其他方法的诊断性能,更适用于肺结节良/恶性的辅助诊断。

The existing deep learning framework used in diagnosing lung cancer still mainly focuses on lung Computed Tomography（CT）images,but it cannot obtain more higher diagnostic rate,when using only one images in the process of daily diagnosis.Therefore,in this paper,a new pulmonary nodule diagnosis method using dual-modal combined with CT and Positron Emission Tomography（PET）deep denoising autoencoder based on extreme learning machine（SDAEELM）was proposed to improve the diagnostic performance effectively.First of all,the method gets discriminative features information separate from the input data CT and PET.Secondly,it inputs CT and PET about candidate lung respectively in whole network.Thirdly,it extracts the high level discriminative features of nodules by alternating stack denoising autoencoder layers.Finally,it makes the fusion strategy of multi-feature fusion as the output of the whole framework.The experiment results show that classification accuracy of the proposed method can reach 92.81%,sensitivities up to 91.75% and specificity up to 1.58%.Meanwhile,the method achieves better discriminative results and is highly suited to be used for pulmonary nodule diagnosis.