CONTEXT: Computer-aided diagnosis (CAD) combining mammographic features from cranio-caudal (CC) and medio-lateral-oblique (MLO) views improve the diagnostic performance of breast cancer. This could help doctors incorrect diagnosis at the earlier stage thereby reducing mortality. AIM: The aim of this study is to propose a breast cancer diagnostic technique for improving the diagnostic accuracy and reducing the false positive rate by fusing mammographic features from CC and MLO views. SETTINGS AND DESIGN: The MLO and CC view mammograms of same patients must be used to extract k-Gabor features and then fused to form a single feature vector. SUBJECTS AND METHODS: Mammograms from the digital database for screening mammography (DDSM) and INbreast datasets are collected. k-Gabor features extracted from both MLO and CC view mammograms are fused serially and reduced by principal component analysis (PCA) or genetic algorithm. The reduced features are classified using a multi-layer perceptron feed forward neural network with backpropagation learning algorithm. STATISTICAL ANALYSIS USED: Various relevant performance metrics such as accuracy, sensitivity, specificity, discriminant power, Mathews correlation coefficient (MCC), F1 score and Kappa are used to analyze the classification results. RESULTS: The accuracy, sensitivity, specificity, discriminant power, MCC, F1 score, and Kappa obtained as 92.5%, 93%, 91.8%, 1.198, 0.845, 0.936, and 0.845, respectively, for DDSM. For INbreast, the above specified metrics are 87.5%, 90.9%, 85.7%, 0.980, 0.741, 0.833, and 0.734, respectively. The results show 4.4%, 4.3%, and 9.4% improvements in accuracy, sensitivity, and specificity, respectively, compared to the previous works. CONCLUSIONS: Detailed analysis of the results implies that the serial fusion of k-Gabor features extracted from MLO and CC views with PCA reduction in CAD significantly improves the diagnostic performance.