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Breast cancer is the second most common cancer among women worldwide.Diagnosis of breast cancer by the pathologists is a time-consuming procedureand subjective. Computer aided diagnosis frameworks are utilized to relievepathologist workload by classifying the data automatically, in which deepconvolutional neural networks (CNNs) are effective solutions. The featuresextracted from activation layer of pre-trained CNNs are called deepconvolutional activation features (DeCAF). In this paper, we have analyzed thatall DeCAF features are not necessarily led to a higher accuracy in theclassification task and dimension reduction plays an important role. Therefore,different dimension reduction methods are applied to achieve an effectivecombination of features by capturing the essence of DeCAF features. To thispurpose, we have proposed reduced deep convolutional activation features(R-DeCAF). In this framework, pre-trained CNNs such as AlexNet, VGG-16 andVGG-19 are utilized in transfer learning mode as feature extractors. DeCAFfeatures are extracted from the first fully connected layer of the mentionedCNNs and support vector machine has been used for binary classification. Amonglinear and nonlinear dimensionality reduction algorithms, linear approachessuch as principal component analysis (PCA) represent a better combination amongdeep features and lead to a higher accuracy in the classification task usingsmall number of features considering specific amount of cumulative explainedvariance (CEV) of features. The proposed method is validated using experimentalBreakHis dataset. Comprehensive results show improvement in the classificationaccuracy up to 4.3% with less computational time. Best achieved accuracy is91.13% for 400x data with feature vector size (FVS) of 23 and CEV equals to0.15 using pre-trained AlexNet as feature extractor and PCA as featurereduction algorithm.

journal of digital imaging

Reduced Deep Convolutional Activation Features (R-DeCAF) in  Histopathology Images to Improve the Classification Performance for Breast  Cancer Diagnosis