Lung cancer stands as the deadliest cancer worldwide, and early detection of pulmonary nodules is the focus of many studies to enhance the survival rate. As with many diseases, deep learning is becoming a commonly used technique for computer-aided diagnosis (CAD) in detecting lung nodules. Most lung CAD systems rely on a detection module followed by a false positive (FP) reduction module (FPR); however, FPR removes FPs as well as true positives (TPs). Thus, as a tradeoff, in order to retain high sensitivity, a large number of FPs remain. In our experience, small pulmonary vessels have been the primary source of FPs. Hence, we propose an additional module cascaded on normal FPR module to specifically reduce the number of FPs due to pulmonary vessel. Utilizing a 3D deep learning architecture, we find that the inclusion of various fields of view (FOVs) improves the accuracy of the chosen model. We explore the impact of the selection of the FOVs, the method used to integrate the features from each FOV, and using the FOV as a data augmentation method. We show that this vessel specific FPR module significantly improves the CAD system’s FP rate while only sacrificing 5% of the previously achieved sensitivity.