VGG19 Demonstrates the Highest Accuracy Rate in a Nine-Class Wound Classification Task Among Various Deep Learning Networks: A Pilot Study

Abstract

Background. Current literature suggests relatively low accuracy of multi-class wound classification tasks using deep learning networks. Solutions are needed to address the increasing diagnostic burden of wounds on wound care professionals and to aid non-wound care professionals in wound management. Objective. To develop a reliable, accurate 9-class classification system to aid wound care professionals and perhaps eventually, patients and non-wound care professionals, in managing wounds. Methods. A total of 8173 training data images and 904 test data images were classified into 9 categories: operation wound, laceration, abrasion, skin defect, infected wound, necrosis, diabetic foot ulcer, chronic ulcer, and wound dehiscence. Six deep learning networks, based on VGG16, VGG19, EfficientNet-B0, EfficientNet-B5, RepVGG-A0, and RepVGG-B0, were established, trained, and tested on the same images. For each network the accuracy rate, defined as the sum of true positive and true negative values divided by the total number, was analyzed. Results. The overall accuracy varied from 74.0% to 82.4%. Of all the networks, VGG19 achieved the highest accuracy, at 82.4%. This result is comparable to those reported in previous studies. Conclusion. These findings indicate the potential for VGG19 to be the basis for a more comprehensive and detailed AI-based wound diagnostic system. Eventually, such systems also may aid patients and non-wound care professionals in diagnosing and treating wounds.