CWD2GAN: Generative Adversarial Network of Chronic Wound Depth Detection for Predicting Chronic Wound Depth

Abstract

Clinically, for observing the healing of the patient’s wound, doctors need to insert a cotton swab into the deepest part of the wound to detect the depth of the wound. This measurement method will cause discomfort to the patient. Therefore, obtaining wound depth information directly from wound images is very important for doctors to understand the degree of wound healing. In this paper, we propose the generative adversarial network of chronic wound depth detection (CWD2GAN) to generate wound depth maps of four different shades of color according to the changes of the wound area in the chronic wound image. In CWD2GAN, the generator, which can generate the wound depth map, is composed of three parts: encoder, decoder, and concatenation. And, the discriminator uses the concept of cGAN. It can not only judge whether the generator produces an image but also know that this image is a depth map. In experimental results, the accuracy, sensitivity, specificity, and precision of CWD2GAN are 84.8%, 84.6%, 84.9%, and 86.3%, respectively. The results indicate that our proposed method can accurately generate the different depths layer in a chronic wound image, and reduce the pain caused by invasive testing for patients.