Utilizing Mask R-CNN for Detection and Segmentation of Oral Diseases

Abstract

In this paper, we demonstrate the application of Mask-RCNN, the state-of-the-art convolutional neural network algorithm for object detection and segmentation to the oral pathology domain. Mask-RCNN was originally developed for object detection, and object instance segmentation of natural images. With this experiment, we show that Mask-RCNN can also be used in a very specialized area such as oral pathology. While the number of oral diseases are numerous and varied in the form of Thrush, Leukoplakia, Lichenplanus, etc., we limited our scope to the detection and instance segmentation of two of the most commonly occurring conditions, herpes labialis (commonly referred to as “cold sore“ and aphthous ulcer (commonly referred to as “canker sore“ This paper aims at detecting and segmenting cold sores and canker sores only. As always, no computer based detection system can be 100% reliable. An accurate diagnosis by a trained health care professional is necessary since several conditions of the mouth including oral cancer may mimic canker sores.

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