Neural network fusion strategies for identifying breast masses

In this work, we introduce the Perceptron Average neural network fusion strategy and implemented a number of other fusion strategies to identify breast masses in mammograms as malignant or benign with both balanced and imbalanced input features. We numerically compare various fixed and trained fusion rules, i.e., the Majority Vote, Simple Average, Weighted Average, and Perceptron Average, when applying them to a binary statistical pattern recognition problem. To judge from the experimental results, the Weighted Average approach outperforms the other fusion strategies with balanced input features, while the Perceptron Average is superior and achieves the goals with lowest standard deviation with imbalanced ensembles. We concretely analyze the results of above fusion strategies, state the advantages of fusing the component networks, and provide our particular broad sense perspective about information fusion in neural networks.