We propose a computer-aided detection (CAD) method for breast cancer screening using convolutional neural network (CNN) and follow-up scans. First, mammographic images are examined by three cascading object detectors to detect suspicious cancerous regions. Then all regional images are fed to a trained CNN (based on the pre-trained VGG-19 model) to filter out false positives. Three cascading detectors are trained with Haar features, local binary pattern (LBP) and histograms of oriented gradient (HOG) separately via an AdaBoost approach. The bounding boxes (BBs) from three featured detectors are merged to generate a region proposal. Each regional image, consisting of three channels, current scan (red channel), registered prior scan (green channel) and their difference (blue channel), is scaled to 224×224×3 for CNN classification. We tested the proposed method using our digital mammographic database including 69 cancerous subjects of mass, architecture distortion, and 27 healthy subjects, each of which includes two scans, current (cancerous or healthy), prior scan (healthy 1 year before). On average 165 BBs are created by three cascading classifiers on each mammogram, but only 3 BBs remained per image after the CNN classification. The overall performance is described as follows: sensitivity = 0.928, specificity = 0.991, FNR = 0.072, and FPI (false positives per image) = 0.004. Considering the early-stage cancerous status (1-year ago was normal), the performance of the proposed CAD method is very promising.