Fusion of Handcrafted and Deep Features for Medical Image Classification

Abstract

Medical image classification has recently attracted increased attention. Effective feature extraction and learning are key means to improve classification performance. However, in the current study, handcrafted feature are mostly designed with intuitive mode, while the deep feature depends on a large amount of training samples and has weak interpretability. To capture more discriminative features for medical image, a novel feature fusion approach, termed multi-layer visual feature fusion (MLVSF), has been proposed on the basis of low-level, mid-level and deep features. More specifically, by fusing the handcrafted and deep features generated by local binary pattern variant, bag-of-visual-words, convolutional neural network, respectively, MLVSF can effectively enhance the discriminating power of features for medical image recognition. Experimental results on two medical image datasets show that MLVSF can improve convolutional neural networks, and achieve a better classification accuracy in comparison with some state-of-the-art methods.