Comparative analysis of logistic regression and artificial neural network for computer-aided diagnosis of breast masses
Comparative analysis of logistic regression and artificial neural network for computer-aided diagnosis of breast masses
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DOI：
10.1016/j.acra.2004.12.016
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发表时间：
2005-04-01
影响因子：
4.8
通讯作者：
Sehgal, CM
中科院分区：
文献类型：
--
作者：
Song, JH;Venkatesh, SS;Sehgal, CM
理由和目的。比较逻辑回归与人工神经网络在乳腺超声计算机辅助诊断中的应用。材料与方法。对24例恶性肿块和30例良性肿块的超声图像进行定量分析，分析肿块边缘清晰度、肿块边缘回声性和肿块边缘角度变化。这些特征和患者的年龄使用两种模式分类器，逻辑回归和人工神经网络来区分恶性和良性肿块。采用受试者工作特征（ROC）分析比较两种方法的疗效。logistic回归分析的ROC曲线下面积Az （+/- SD）为0.853 +/- 0.059,95%置信限为0.76 -0.950。人工神经网络分析的ROC曲线下面积为0.856 +/- 0.058,95%置信限为0.734-0.936。虽然逻辑回归和人工神经网络的ROC曲线下面积相同，但两种曲线的形状不同。在95%的灵敏度下，人工神经网络的特异性为76.5%，而逻辑回归的特异性为64.7%。通过ROC曲线下面积测量，逻辑回归与人工神经网络的性能没有差异。然而，在95%的固定灵敏度下，人工神经网络与逻辑回归值相比具有更高的特异性（12%）。
Rationale and Objective. To compare logistic regression and artificial neural network for computer-aided diagnosis on breast sonograms.Materials and Methods. Ultrasound images of 24 malignant and 30 benign masses were analyzed quantitatively for margin sharpness, margin echogenicity, and angular variation in margin. These features and age of patients were used with two pattern classifiers, logistic regression, and an artificial neural network to differentiate between malignant and benign masses. The performance of two methods was compared by receiver operating characteristic (ROC) analysis.Results. The area under the ROC curve Az (+/- SD) of the logistic regression analysis was 0.853 +/- 0.059 with 95% confidence limit (0.760-0.950). The area under the ROC curve of the artificial neural network analysis was 0.856 +/- 0.058 with 95% confidence limit (0.734-0.936). Although both the logistic regression and the artificial neural network had the same area under the ROC curve, the shapes of two curves were different. At 95% sensitivity, the artificial neural network had 76.5% specificity, whereas logistic regression had 64.7% specificity.Conclusion. There was no difference in performance between logistic regression and the artificial neural network as measured by the area under the ROC curve. However, at a fixed 95% sensitivity, the artificial neural network had higher (12%) specificity compared with logistic regression value.