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Abstract：
Breast cancer is the second most common cancer among women worldwide, and the diagnosis by pathologists is a time-consuming procedure and subjective. Computer-aided diagnosis frameworks are utilized to relieve pathologist workload by classifying the data automatically, in which deep convolutional neural networks (CNNs) are effective solutions. The features extracted from the activation layer of pre-trained CNNs are called deep convolutional activation features (DeCAF). In this paper, we have analyzed that all DeCAF features are not necessarily led to higher accuracy in the classification task and dimension reduction plays an important role. We have proposed reduced DeCAF (R-DeCAF) for this purpose, and different dimension reduction methods are applied to achieve an effective combination of features by capturing the essence of DeCAF features. This framework uses pre-trained CNNs such as AlexNet, VGG-16, and VGG-19 as feature extractors in transfer learning mode. The DeCAF features are extracted from the first fully connected layer of the mentioned CNNs, and a support vector machine is used for classification. Among linear and nonlinear dimensionality reduction algorithms, linear approaches such as principal component analysis (PCA) represent a better combination among deep features and lead to higher accuracy in the classification task using a small number of features considering a specific amount of cumulative explained variance (CEV) of features. The proposed method is validated using experimental BreakHis and ICIAR datasets. Comprehensive results show improvement in the classification accuracy up to 4.3% with a feature vector size (FVS) of 23 and CEV equal to 0.15.
摘要：
乳腺癌是全球女性中第二常见的癌症，病理学家的诊断是一个耗时且主观的过程。计算机辅助诊断框架通过自动分类数据来减轻病理学家的工作量，其中深度卷积神经网络(CNN)是有效的解决方案。从预先训练的CNN的激活层提取的特征称为深度卷积激活特征(DeCAF)。在本文中,我们已经分析了所有的DeCAF特征在分类任务中不一定会导致更高的准确性，降维起着重要的作用。为此，我们提出了减少的DeCAF（R-DeCAF），并应用不同的降维方法，通过捕捉DeCAF特征的本质，实现特征的有效组合。这个框架使用预先训练的CNN，如AlexNet，VGG-16和VGG-19作为迁移学习模式下的特征提取器。DeCAF特征是从上述CNN的第一个全连接层中提取的，并采用支持向量机进行分类。在线性和非线性降维算法中，诸如主成分分析(PCA)的线性方法代表了深层特征之间的更好组合,并且在考虑特征的特定量的累积解释方差(CEV)的使用少量特征的分类任务中导致更高的准确度。使用实验BreakHis和ICIAR数据集验证了所提出的方法。综合结果表明，在特征向量大小（FVS）为23和CEV等于0.15的情况下，分类精度提高了4.3％。
