Computed tomography (CT) has recently replaced conventional X-ray as the primary screening tool for lung cancer because it has been shown to reduce mortality by as much as 20% in high risk patients (Aberle et al. , 2011). Unfortunately, there is a high false positive rate (FPR) associated with CT screening. At least one “lung nodule” is detected in half of all CT scans, but only 10% of these “nodules” are in fact cancerous. The goal of this project was to improve both localization and classification of lung nodules (with deep learning methods/pipelines) using the Lung Nodule Analysis (LUNA16) dataset. We were primarily concerned with two metrics: (i.) nodule detection and localization and (ii.) nodule false positive reduction.