首页> 中文期刊>国际生物医学工程杂志 >基于深度信念网络的肺结节图像自动识别方法

基于深度信念网络的肺结节图像自动识别方法

摘要

目的 提出一种基于深度信念网络(DBN)的自动识别肺结节的方法,以提高肺结节的检测准确性.方法 为满足DBN的训练样本需求,建立了由专业医生判别的4 000张肺结节图像组成的数据库,并使用虚拟样本技术对样本数据库进行了扩充,其中通过对人工判读的感兴趣区域(ROI)进行旋转、缩放、平移或平移、缩放、旋转、复合中2种或以上的组合操作生成新的样本.最后,将样本库中的部分样本输入卷积神经网络分类器,通过优化网络参数,输出疑似肺结节所在的ROI.结果 使用虚拟样本扩充的方法将训练样本库的样本量扩展为40 000.基于该方法获取的训练数据库,DBN识别肺结节的检测准确率为90%,假阳性率为0.4%.结论 虚拟样本技术可有效提高训练数据库的建立效率.采用基于DBN的CAD技术检测肺结节的准确性较高,可使医生只重点关注检测出有肺结节的区域,从而有效提升医生的诊断效率.%Objective To propose a method based on deep belief network (DBN) to automatically identify pulmonary nodules so as to improve the detection accuracy of pulmonary nodules.Methods To meet the training sample requirements of DBN,a database of 4 000 lung nodule images identified by professional doctors was established,and the sample database was expanded using virtual sample technology.In this technology,new samples of the database were generated from the manually recognized region of interest (ROI) by rotation,scaling and panning,or by a series of combinations of two or more operations of panning,scaling,rotation,and compositing.Finally,some samples from the sample database were input into the convolutional neural network classifier,and the ROI of the suspected pulmonary nodule was output by optimizing the network parameters.Result The sample size of the training sample database was expanded to 40 000 using the virtual sample expansion.Based on the training database obtained by this method,the detection accuracy of DBN for identifying pulmonary nodules was 90%,and the false positive rate was 0.4%.Conclusion Virtual sample technology can effectively improve the efficiency of training database establishment.The accuracy of using DBN-based CAD technology to detect pulmonary nodules is high,allowing doctors to focus only on areas where lung nodules are detected,thus effectively improving the efficiency of diagnosis.

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