Breast mass lesion classification in mammograms by transfer learning

Abstract

Automatic classification of breast mass lesions in mammographic images remains an unsolved problem. This paper explored the technique of transfer learning to tackle this problem. It utilized the convolutional neural network (CNN) of GoogLeNet and AlexNet pre-trained on a large-scale visual database. The performance was evaluated a new dataset in terms of the area under the receiver operating characteristic curves (AUC). Results demonstrate that GoogLeNet (AUC=0.88) outperforms AlexNet (AUC=0.83) and other state-of-the-art traditional approaches in breast cancer diagnosis. The technique of transfer learning not only overcomes the unsatisfactory performance of traditional approaches, but also breaks the obstacle of limited samples for building deep CNNs.