Fluorescent biomarkers are important indicators of disease, but imaging them can require specialized and often-expensive devices. Periodontal and dental diseases resulting from microbial plaque biofilms, if diagnosed early with biomarker images and expert knowledge, can be treated to prevent occurrences of serious systemic illnesses. We report two convolutional neural network classifiers trained with dentist annotations of disease signatures and fluorescent porphyrin biomarker images to identify dental plaque in white light images as a per-pixel binary classification task. The classifiers were trained and tested with millions of image patches from two datasets collected from 27 consenting adults using handheld intraoral cameras. The areas under the receiver operating characteristic curves for the test sets were calculated to be 0.7694 and 0.8720. Once trained, the classifiers predict the location of plaque in white light images without requiring specialized biomarker imaging devices or expert intervention. This generalized approach can be useful in other domains where diagnostic biomarker predicting can augment expert knowledge using standard white light images.