Segmentation and Feature Extraction of Fingernail Plate and Lunula Based on Deep Learning

Abstract

AbstractThis paper proposes a novel deep learning method for accurate segmentation of the fingernail plate and lunula. To achieve this, we designed a new network structure called Nailnet, which segments the fingernail from images of the whole hand. The results show that Nailnet achieved an Intersection over Union (IoU) score of 0.9529 and an accuracy of 0.9725 for fingernail plate segmentation. For lunula segmentation, Nailnet achieved an IoU score of 0.7784 and an accuracy of 0.8846. Additionally, Nailnet successfully recognized the fingernail index, enabling the extraction of various fingernail phenotypes, including plate color, plate shape, lunula color, and lunula proportion.