Mammography is the most widely used gold standard for screening breast cancer, where, mass detection is considered as the prominent step. Detecting mass in the breast is, however, an arduous problem as they usually have large variations between them in terms of shape, size, boundary, and texture. In this literature, the process of mass detection is automated with the use of transfer learning techniques of Deep Convolutional Neural Networks (DCNN). Pre-trained VGG19 network is used to extract features which are then followed by bagged decision tree for features selection and then a Support Vector Machine (SVM) classifier is trained and used for classifying between the mass and non-mass. Area Under ROC Curve (AUC) is chosen as the performance metric, which is then maximized during classifier selection and hyper-parameter tuning. The robustness of the two selected type of classifiers, C-SVM, and \u{psion}-SVM, are investigated with extensive experiments before selecting the best performing classifier. All experiments in this paper were conducted using the INbreast dataset. The best AUC obtained from the experimental results is 0.994 +/- 0.003 i.e. [0.991, 0.997]. Our results conclude that by using pre-trained VGG19 network, high-level distinctive features can be extracted from Mammograms which when used with the proposed SVM classifier is able to robustly distinguish between the mass and non-mass present in breast.
翻译:乳房造影是用来筛查乳腺癌的最广泛使用的黄金标准,在那里,大规模检测被认为是突出的一步。然而,在乳房中检测质量是一个棘手的问题,因为它们在形状、大小、边界和质谱方面通常存在很大的差异。在这一文献中,通过使用深革命神经网络(DCNN)的转移学习技术,质量检测过程是自动化的。先受过训练的VGG19网络用来提取特征,然后用包装决定树作为选择特征的标志,然后用支持矢量机分类器(SVM)进行训练,用于对质量和非质量进行分类。ROC曲线(AUC)下的区域通常在形状、大小、边界和质谱方面差异很大。在分类选择分解器和超参数时,质量检测过程是最大化的。 预选的两种类型的分类器的坚固性(C-S-SVVM)-SVM网络在选择最佳性能分类方法之前进行广泛的实验。 本文中的所有实验都是使用INCRUC Curverial数据集的非质量进行。