Tumor Classification by Deep Polynomial Network and Multiple Kernel Learning on Small Ultrasound Image Dataset

Abstract

Ultrasound imaging is a most common modality for tumor detection and diagnosis. Deep learning (DL) algorithms generally suffer from the small sample problem. The traditional texture feature extraction methods are still commonly used for small ultrasound image dataset. Deep polynomial network (DPN) is a newly proposed DL algorithm with excellent feature representation, which has the potential for small dataset. However, the simple concatenation of the learned hierarchical features from different layers in DPN limits its performance. Since the features from different layers in DPN can be regarded as heterogeneous features, they then can be effectively integrated by multiple kernel learning (MKL) methods. In this work, we propose a DPN and MKL based feature learning and classification framework (DPN-MKL) for tumor classification on small ultrasound image dataset. The experimental results show that DPN-MKL algorithm outperforms the commonly used DL algorithms for ultrasound image based tumor classification on small dataset.