In 2016, World Health Organization (WHO) estimated that diabetes is the seventh leading cause of death causing 1.6 million casualties globally. In this paper, we propose a non-invasive solution through a Convolutional Neural Network (CNN)-based Deep Learning classifier. We use the scalograms generated out of transmissive Photoplethysmography (PPG) signals collected from MIMIC-III database to diagnose diabetes. Different sets of inputs were sent into a slightly modified VGGNet model, which were trained over data from 584 patients. We provide a probabilistic score of diabetes for every patient, which is further used for classifying patients into diabetic and nondiabetic. The best model obtained using a combination of PPG signals, hypertension classification, age and gender as inputs produced an accuracy of 76.34% and area under the curve (AUC) of 0.830 on 224 test patients. In our knowledge, this is among the first CNN-based approaches in the literature to detect diabetes using MIMIC-III waveforms dataset with a good performance.