The successful steady-state operation of burning fusion plasmas in planned future devices such as the ITER tokamak requires understanding of fast-ion physics. Alfven eigenmodes are special cases of plasma waves driven by fast ions that are important to identify and control since they can lead to loss of confinement and potential damage to the inner walls of a plasma device. The goal of this work is to compare machine learning-based systems trained to classify Alfven eigenmodes using CO2interferometer data from a labelled database on the DIII-D tokamak. A Long-Short Term Memory (LSTM) network is trained from scratch using simple spectrogram representations of the CO2 phase data. The model is trained using a single chord (sequence) per training step. Results show a total true positive rate of = 90% and a false positive rate of = 18%. This paper demonstrates the potential of applying machine learning models to detect and identify different classes of Alfven eigenmodes for real-time applications in steady-state plasma operations that could potentially drive actuators to mitigate Alfven eigenmode impacts.