Breast tumor classification in ultrasound images using support vector machines and neural networks

Abstract Introduction The use of tools for computer-aided diagnosis (CAD) has been proposed for detection and classification of breast cancer. Concerning breast cancer image diagnosing with ultrasound, some results found in literature show that morphological features perform better than texture features for lesions differentiation, and indicate that a reduced set of features performs better than a larger one. Methods This study evaluated the performance of support vector machines (SVM) with different kernels combinations, and neural networks with different stop criteria, for classifying breast cancer nodules. Twenty-two morphological features from the contour of 100 BUS images were used as input for classifiers and then a scalar feature selection technique with correlation was used to reduce the features dataset. Results The best results obtained for accuracy and area under ROC curve were 96.98% and 0.980, respectively, both with neural networks using the whole set of features. Conclusion The performance obtained with neural networks with the selected stop criterion was better than the ones obtained with SVM. Whilst using neural networks the results were better with all 22 features, SVM classifiers performed better with a reduced set of 6 features.

摘要 引言
计算机辅助诊断（computer-aided diagnosis, CAD）工具已被提出用于乳腺癌的检测与分类。针对乳腺超声影像的乳腺癌诊断任务，已有文献报道的研究结果显示，在病灶鉴别任务中，形态学特征的表现优于纹理特征，且精简后的特征子集的性能优于全量特征集合。

方法
本研究针对乳腺结节分类任务，评估了采用不同核函数组合的支持向量机（Support Vector Machines, SVM）以及采用不同停止准则的神经网络的分类性能。本研究使用100幅乳腺超声（Breast Ultrasound, BUS）图像轮廓提取的22个形态学特征作为分类器的输入，随后采用基于相关性的标量特征选择技术对特征数据集进行精简。

结果
采用全量特征集合的神经网络模型取得了最优的分类准确率与ROC曲线下面积，分别为96.98%与0.980。

结论
采用选定停止准则的神经网络模型的分类性能优于支持向量机。在使用神经网络时，全量22个特征即可取得最优结果；而支持向量机分类器则在精简为6个特征的子集时表现更佳。