Lung cancer is the third most common cancer and the leading cause of cancer-related death in America. This cancer has a high lethality with an overall survival of 16% at five years. Symptoms are nonspecific, so diagnosis is usually delayed. To achieve earlier diagnosis and initiate treatment at a non-advanced stage of the cancer to reduce mortality, low-dose computed tomography (CT) scans are performed. Therefore, advanced image processing and machine learning techniques are required since the high volume of images generated by medical equipment causes the review of a lot of information to make a medical diagnosis. For diagnosis, the images are analyzed by specialists in order to find nodules, measure them and evaluate them. However, the nodules found in the lungs have different shapes, dimensions and textures, which makes identification difficult. For this reason, this paper presents the implementation, analysis and evaluation of two deep learning techniques for the detection of pulmonary nodules in CT scans, resulting in prediction models with a high percentage of accuracy.