Deep-learning-based Capillary Detection

It is crucial to diagnose diabetes in the early phase because it can cause serious complications if not treated early. Nailfold capillaries are capillaries that are located under the proximal nail fold area. Studies have shown that different types of abnormal patterns of nail fold capillaries can be important factors when diagnosing various diseases or conditions. Therefore, this study proposes a system that can detect the nailfold capillary morphology of type 2 diabetes mellitus in a non-invasive way utilizing an object detection model. We trained an object detection model YOLOv5 with bi-directional feature pyramid network (Bi-FPN) as the neck part, which is an improved version of the open-source object detector YOLOv5 to detect capillaries and their corresponding morphology types including hairpin, crossing, bushy, and tortuous capillaries with mAP@50 0.74. We expect the proposed system to aid in the early diagnosis of diabetes in the future.