This paper describes the participation of the MedGIFT/UPB group in the ImageCLEF 2017 tuberculosis task. This task includes two subtasks: (1) multi–drug resistance detection (MDR), with the goal of determining the probability of a tuberculosis patient having a resistant form of tuberculosis and (2) tuberculosis type detection (TBT), with the goal of classifying each tuberculosis patient into one of the following five types: infiltrative, focal, tuberculoma, miliary and fibro–cavernous. Two runs were submitted for the TBT subtask and one run for the MDR subtask. Both of them use visual features learned with a deep learning approach directly from slices of patient CT (Computed Tomography) scans. For the TBT subtask the submitted runs obtained the 3rd and 8th position out of 23 runs submitted for this task, with a top Kappa value of 0.2329. In the MDR subtask, the proposed approach obtained the 7th position according to the accuracy (0.5352) out of 20 participant runs. Three main techniques were exploited during model training: pre–training the last layer of a neural network, small learning rates and data augmentation techniques. Data augmentation resulted in an effective and efficient data transformation that enhanced small lesions in the full image space.