We introduce a non-parametric linear decision fusion called Perceptron Average (PA) for breast cancer diagnosis. We concretely compare the accuracy between both two fusion strategies for breast cancer diagnosis. The PA fusion demonstrates a higher overall diagnostic accuracy versus the Weighted Average fusion, and the PA fusion method also exhibits a better capability of generalization when a casualty of training data sizes. Moreover, the PA fusion gains a larger area covered by its Receiver Operating Characteristic curve.