Mild Cognitive Impairment (MCI) brings an increased risk of progressing to Alzheimer’s disease (AD). Early identification of a risk of MCI progression could help patients get early treatment to slow progress of the disease. We used 3D Stereotactic Surface Projections (SSP) of Positron Emission Tomography (PET) brain images to train a classification model to identify MCI patients at risk of progressing to AD, which achieved 88.0% accuracy, 85.3% sensitivity, and 90.6% specificity. For model transparency purposes, we generated saliency map explanations from the trained model and evaluated these using radiologist feedback.