Chronic Wound Assessment System Using an Improved UPerNet Model

Abstract

Wound assessment plays a crucial role in the healing process. Traditional methods for wound assessment, relying on manual judgment and recording, often yield inaccurate outcomes and require specialized medical equipment. This will result in increasing regular hospital visits and pose a series of challenges, such as delayed examinations, heightened infection risks, and increased costs. Thus, a real-time, portable, and convenient wound assessment system is essential for healing chronic wounds and reducing complications. This paper proposes an improved UPerNet network for wound tissue segmentation, which consists of three modules: Feature-aligned Pyramid Network (FaPN), Kernel update head, and Convolutional Block Attention Module (CBAM). The FaPN is employed to address feature misalignment. The Kernel update head is based on K-Net and dynamically updates convolutional kernel weights. The CBAM module is adopted to attend to crucial features. Ablation studies and comparative studies show that this method can achieve superior performance in wound tissue segmentation compared to other common segmentation models. Meanwhile, based on this method, we develop a mobile application. By using this system, patients can easily upload wound images for assessment, facilitating the convenient tracking of wound healing progress at home, thereby reducing medical expenses and hospital visitation times.