Deep transfer with minority data augmentation for imbalanced breast cancer dataset

Abstract

Clinical diagnosis of breast cancer is a challenging problem in the biomedical domain. The BreakHis breast cancer histopathological image dataset consists of two classes: Benign (Minority class) and Malignant (Majority class). The imbalanced class distribution results in the degradation of performance of the classifier model due to biased classification towards the majority class. To tackle this problem, a novel learning strategy that involves a deep transfer network has been proposed in this paper, in collaboration with Deep Convolution Generative Adversarial network (DCGAN). DCGAN is used in the initial phase for data augmentation of the minority class only. The dataset, with the class distribution now balanced, is applied as input to the deep transfer network. The proposed deep transfer architecture has at its core, the initial pre-trained layers (until block 4 pool layer) of the VGG16 deep network architecture pre-trained on the ImageNet object classification dataset. The higher end of our transfer network comprises of Batch Normalization, 2D Convolutional (CONV2D) layer, Global Average Pooling 2D, Dropout and Dense layers that are added to enhance the network’s performance. Experiments on the benchmark BreakHis dataset for different magnification factors: 40X, 100X, 200X and 400X validate the efficiency of the proposed deep transfer learning approach due to the high scores achieved as compared to the state-of-the-art deep networks.