Wu Y C, Doi K, Giger M L
Kurt Rossmann Laboratories for Radiologic Image Research, Department of Radiology, University of Chicago, IL, USA.
J Digit Imaging. 1995 May;8(2):88-94. doi: 10.1007/BF03168131.
Radiologists can fail to detect up to 30% of pulmonary nodules in chest radiographs. A back-propagation neural network was used to detect lung nodules in digital chest radiographs to assist radiologists in the diagnosis of lung cancer. Regions of interest (ROIs) that contained nodules and normal tissues in the lung were selected from digitized chest radiographs by a previously developed computer-aided diagnosis (CAD) scheme. Different preprocessing techniques were used to produce input data to the neural network. The performance of the neural network was evaluated by receiver operating characteristic (ROC) analysis. We found that subsampling of original 64- x 64-pixel ROIs to smaller 8- x 8-pixel ROIs provides the optimal preprocessing for the neural network to distinguish ROIs containing nodules from false-positive ROIs containing normal regions. The neural network was able to detect obvious nodules very well with an Az value (area under ROC curve) of 0.93, but was unable to detect subtle nodules. However, with a training method that uses different orientations of the original ROIs, we were able to improve the performance of the neural network to detect subtle nodules. Artificial neural networks have the potential to serve as a useful classifier to help to eliminate the false-positive detections of the CAD scheme.
放射科医生在胸部X光片中可能漏诊高达30%的肺结节。使用反向传播神经网络来检测数字化胸部X光片中的肺结节，以协助放射科医生诊断肺癌。通过先前开发的计算机辅助诊断（CAD）方案，从数字化胸部X光片中选择包含肺结节和正常组织的感兴趣区域（ROI）。采用不同的预处理技术来生成神经网络的输入数据。通过接收者操作特征（ROC）分析评估神经网络的性能。我们发现，将原始64×64像素的ROI下采样为较小的8×8像素的ROI，为神经网络区分包含结节的ROI和包含正常区域的假阳性ROI提供了最佳预处理。该神经网络能够很好地检测明显的结节，Az值（ROC曲线下面积）为0.93，但无法检测到微小的结节。然而，通过使用原始ROI不同方向的训练方法，我们能够提高神经网络检测微小结节的性能。人工神经网络有潜力作为一种有用的分类器，帮助消除CAD方案中的假阳性检测。
