A deep feature based framework for breast masses classification.

Characteristic classification of mass plays a role of vital importance in diagnosis of breast cancer. The existing computer aided diagnosis (CAD) methods used to benefit a lot from low-level or middle-level features which are not that good at the simulation of real diagnostic processes, adding difficulties in improving the classification performance. In this paper, we design a deep feature based framework for breast mass classification task. It mainly contains a convolutional neural network (CNN) and a decision mechanism. Combining intensity information and deep features automatically extracted by the trained CNN from the original image, our proposed method could better simulate the diagnostic procedure operated by doctors and achieved state-of-art performance. In this framework, doctors' global and local impressions left by mass images were represented by deep features extracted from two different layers called high-level and middle-level features. Meanwhile, the original images were regarded as detailed descriptions of the breast mass. Then, classifiers based on features above were used in combination to predict classes of test images. And outcomes of classifiers based on different features were analyzed jointly to determine the types of test images. With the help of two kinds of feature visualization methods, deep features extracted from different layers illustrate effective in classification performance and diagnosis simulation. In addition, our method was applied to DDSM dataset and achieved high accuracy under two objective evaluation measures.