A pyramid machine learning model for polyp classification via CT colonography

In this article, we propose a pyramid multilayer machine learning method to combine classification and feature selection into the same model for polyp classification. This model provides a solution to pick the best attributes from three different texture features to form a new descriptor set with much better classification results. Generally, this method has several good properties including generalization, extendibility, and monotonicity. From its performance, the original metric image descriptor (MD) and the post-histogram-equalized metric image descriptor (PMD) form a descriptor pair as the preliminary unit of this pyramid framework. This model is driven by a feature merging performance unit run iteratively until the final results are obtained. After every feature merging step, a new attribute group is selected to construct a shorter but much stronger new descriptor. To reach this purpose, a forward selection method is adopted only to select attributes from every descriptor with positive gains for classification. Therefore, this feature merging performance provides a guarantee of the classification's monotonicity in the practice. In our experiments, a simple scheme is designed to illustrate its construction and performance. Three image metrics are selected including intensity, gradient and curvature which are put into the gray-level co-occurrence matrix (CM) model to construct polyp descriptors. Random forest is chosen as the classifier and Gini coefficient is used to be the importance score. The AUC (area under the curve of receiver operating characteristics) scores are our evaluation measure. Experimental results showed that the pyramid learning model outperforms other methods over 4%-6% by AUC scores.