Oral cancer is the sixth most common cancer worldwide, predominantly seen in low and middle-income countries (LMIC). Two thirds of all cases are detected at a late stage when prognosis and treatment outcomes are poor. Oral lesions are commonly detected by visual inspection, followed by invasive surgical biopsy and time-consuming histopathological analysis. Optical coherence tomography (OCT), a minimally invasive tomographic imaging technology, can be used to non-invasively identify premalignant or malignant change in the oral mucosa. In this study, a mobile OCT imaging system was designed, constructed, and tested for its performance as a point-of-care oral diagnostic device in an LMIC. 20 patients with suspicious oral lesions and 10 healthy subjects were enrolled in this pilot study. Two-dimensional (2-D) OCT images as well as clinical examination data, risk habit history, and histopathology were collected. OCT images for healthy oral mucosa, dysplasia, and malignancy were evaluated in a blinded fashion by visual scoring and computed image processing techniques. It was found that the OCT image processing algorithm performed at or exceeded the performance of visual observer scoring of OCT images.