用于肺结节影像分类识别的DBN与CNN的比较研究

Comparative Study of DBN and CNN for Pulmonary Nodule Image Recognition

针对肺结节图像的分类识别精度和效率问题,分别将CNN(Convolution Neural Network)模型和DBN(Deep Belief Network)模型用于肺结节分类识别,并评估不同的深度学习模型在肺结节图像分类方面的性能。首先,实验将预处理过的训练集和标签分别输入到CNN模型和DBN模型,达到训练模型的目的;其次,将测试集输入到参数最优的模型中,比较两种模型测试集分类的准确率、敏感性和特异性,并分析两种模型的分类识别性能。最后,从分类准确率、敏感性和特异性3个指标以及时间复杂度来分析比较两种模型,发现CNN模型在肺结节图像分类识别上更有优越性。

Aiming at the classification and recognition accuracy and efficiency of pulmonary nodule images,CNN model and DBN model were used to classify pulmonary nodules,and the performance of different deep learning models in pulmonary nodule image classification was evaluated.Firstly,the experiment input the pre-processed training set and label into the CNN model and the DBN model respectively to achieve the purpose of training the models.Secondly,the test set was input into the parameter-optimized model,and the accuracy,sensitivity and specificity of the classification of the two models were compared.What’s more,the classification and recognition performance of the two models was analyzed.Finally,the two models were analyzed and compared based on the three indicators:classification accuracy,sensitivity and specificity,as well as time complexity.It is found that the CNN model is more advantageous in the classification and recognition of pulmonary nodules.