In the next decade, high-luminosity upgrades to the LHC will confront detectors with an order of magnitude increase in particle collisions. This will push track reconstruction software and hardware beyond current capabilities. The current track reconstruction approaches based on track seeding and track following allow for large contingency and hence are not optimal in terms of computational efficiency. Early fake classification, especially during the first stages of track reconstruction offer viable opportunities for a faster, more efficient reconstruction. In an attempt to harness multiple advantages in a single approach, we investigate the applicability of Deep Neural Networks (DNNs) to the classification of track seeds. A DNN offers not just inherent parallelizability and execution on dedicated hardware, but also the possibility to improve rejection rates for improper seeds and thereby free up time for any actual track reconstruction. This approach is underpinned by the surge of high performance and free to use deep learning frameworks, which have matured over the last years.