Effectiveness of Semi-Supervised Active Learning in Automated Wound Image Segmentation.

ABSTRACT: Appropriate wound management shortens the healing times and reduces the management costs, benefiting the patient in physical terms and potentially reducing the healthcare system's economic burden. Among the instrumental measurement methods, the image analysis of a wound area is becoming one of the cornerstones of chronic ulcer management. Our study aim is to develop a solid AI method based on a convolutional neural network to segment the wounds efficiently to make the work of the physician more efficient, and subsequently, to lay the foundations for the further development of more in-depth analyses of ulcer characteristics. In this work, we introduce a fully automated model for identifying and segmenting wound areas which can completely automatize the clinical wound severity assessment sta

SUBMITTER: Curti N 

PROVIDER: S-EPMC9821322 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature