A new classifier fusion method based on confusion matrix and classification confidence for recognizing common CT imaging signs of lung diseases

Common CT Imaging Signs of Lung Diseases (CISL) are defined as the imaging signs that frequently appear in lung CT images from patients and play important roles in the diagnosis of lung diseases. This paper proposes a new method of multiple classifier fusion to recognize the CISLs, which is based on the confusion matrices of the classifiers and the classification confidence values outputted by the classifiers. The confusion matrix reflects the historical reliability of decision-making of a classifier, while the difference between the classification confidence values for competing classes reflects the current reliability of its decision-making. The two factors are merged to obtain the weights of the classifiersa' classification confidence values for the input pattern. Then the classifiers are fused in a weighted-sum form. In our experiments of CISL recognition, we combine three types of classifiers: the Max-Min posterior Pseudo-probabilities (MMP), the Support Vector Machine (SVM) and the Bagging. Our method behaved better than not only each of the three single classifier but also the AdaBoost with SVM based weak learners. It shows that the proposed method is effective and promising.