Computed tomography (CT) is an important and valuable tool for detecting and diagnosing lung cancer at an early stage. Commonly, CT screenings with lower dose and resolution are used for preliminary screening. In particular, many hospitals in smaller towns only provide CT screenings at low resolution. However,when patients are diagnosed with suspected cancer, they are transferred or recommended to larger hospitals for more sophisticated examinations with high-resolution CT scans. Therefore, multi-resolution CT images deserve attention and are critical in clinical practice. Currently, the available open source datasets only contain high-resolution CT screening images. To address this problem, a multi-resolution CT screening image dataset called the DeepLNDataset is constructed. A three-level labeling criterion and a semi-automatic annotation system are presented to guarantee the correctness and efficiency of lung nodule annotation. Moreover, a novel framework called DeepLN is proposed to detect lung nodules in both low-resolution and high-resolution CT screening images. The multi-level features are extracted by a neural-network based detector to locate the lung nodules. Hard negative mining and a modified focal loss function are employed to solve the common category imbalance problem. A novel non-maximum suppression based ensemble strategy is proposed to synthesize the results from multiple neural network models trained on CT image datasets of different resolutions. To the best of our knowledge, this is the first work that considers the influence of multiple resolutions on lung nodule detection. The experimental results demonstrate that the proposed method can address this issue well. Display omitted