A multi-stage fusion strategy for multi-scale GLCM-CNN model in differentiating malignant from benign polyps

Computer aided diagnosis (CADx) of polyps has shown great potential to advance the computed tomography colonography (CTC) technique with diagnostic capability. Facing the problem of numerous uncertainties such as polyp size, shape, and orientation in CTC, GLCM-CNN has been proved to be an effective deep learning based tumor classification method, where convolution neural network (CNN) makes decision based on the texture pattern encoded in gray level co-occurrence matrix (GLCM) containing 13 directions. The 13 directional GLCM, by sampling displacement, can be classified into 3 subgroups. Based on our evaluation on the information encoded in the three subgroups, we propose a multi-stage fusion CNN model, which makes the final decision based on two types of features, i.e. (1) a gate module selected group-specific features and (2) fused features learnt from all the features from three groups. On our polyp dataset, which contains 87 polyp masses, our proposed method outperforms both single sub-group based and 13 directional GLCM based CNN model by at least 1.3% in AUC by the average of 20 times 2 fold cross validation experiment results.