Automatic Classification of Wound Images Showing Healing Complications: Towards an Optimised Approach for Detecting Maceration.

This study aims to advance the field of digital wound care by developing and evaluating convolutional neural network (CNN) architectures for the automatic classification of maceration, a significant wound healing complication, in 458 annotated wound images. Detection and classification of maceration can improve patient outcomes. Several CNN models were compared and MobileNetV2 emerged as the top-performing model, achieving the highest accuracy despite having fewer parameters. This finding underscores the importance of considering model complexity relative to dataset size. The study also explored the role of image cropping and the use of Grad-CAM visualizations to understand the decision-making process of the CNN. From a medical perspective, results indicate that employing CNNs for classification of maceration may enhance diagnostic accuracy and reduce the clinicians' time and effort.