Penedo M G, Carreira M J, Mosquera A, Cabello D
Computing Department, A Coruña University Informatics School, Campus de Elviña, Spain.
IEEE Trans Med Imaging. 1998 Dec;17(6):872-80. doi: 10.1109/42.746620.
In this work, we have developed a computer-aided diagnosis system, based on a two-level artificial neural network (ANN) architecture. This was trained, tested, and evaluated specifically on the problem of detecting lung cancer nodules found on digitized chest radiographs. The first ANN performs the detection of suspicious regions in a low-resolution image. The input to the second ANN are the curvature peaks computed for all pixels in each suspicious region. This comes from the fact that small tumors possess and identifiable signature in curvature-peak feature space, where curvature is the local curvature of the image data when viewed as a relief map. The output of this network is thresholded at a chosen level of significance to give a positive detection. Tests are performed using 60 radiographs taken from routine clinic with 90 real nodules and 288 simulated nodules. We employed free-response receiver operating characteristics method with the mean number of false positives (FP's) and the sensitivity as performance indexes to evaluate all the simulation results. The combination of the two networks provide results of 89%-96% sensitivity and 5-7 FP's/image, depending on the size of the nodules.
在这项工作中，我们基于两级人工神经网络（ANN）架构开发了一种计算机辅助诊断系统。该系统专门针对数字化胸部X光片中肺癌结节的检测问题进行了训练、测试和评估。第一个人工神经网络在低分辨率图像中执行可疑区域的检测。第二个人工神经网络的输入是为每个可疑区域中的所有像素计算的曲率峰值。这是因为小肿瘤在曲率峰值特征空间中具有可识别的特征，在该空间中，当将图像数据视为地形图时，曲率是图像数据的局部曲率。该网络的输出在选定的显著性水平上进行阈值处理，以给出阳性检测结果。使用从常规诊所获取的60张X光片进行测试，其中有90个真实结节和288个模拟结节。我们采用自由响应接收器操作特性方法，以平均假阳性数（FP）和灵敏度作为性能指标来评估所有模拟结果。根据结节的大小，这两个网络的组合提供了89%-96%的灵敏度和每幅图像5-7个假阳性的结果。
