A deep learning based experimental framework for automatic staging of pressure ulcers from thermal images

Pressure ulcers can be life-threatening, and accurate staging is crucial for proper treatment. Manual methods for ulcer detection and staging are subjective and prone to errors. Since the wound temperature holds valuable and informative data, we propose thermal imaging as a tool to detect the stages of pressure ulcers automatically. This paper implements a deep learning-based framework for the automatic detection and classification of pressure ulcer stages 2–4 using thermal images. 450 thermal images were used to train the classification model. The framework combines 'R50-FPN', retrained using transfer learning, for ulcer detection and cropping. An ensemble model is used for ulcer stage classification. The model was trained on a dataset consisting of 68 original images and their augmentations. The proposed method achieves a mAP for object detection of 0.70 and an ulcer stage classification accuracy of 0.9545 during evaluation. Class activation maps were also employed to study the model performance further, indicating that the trained model focuses on the wound periphery and the wound bed for classification. The results show a significant improvement in classification accuracy when compared with related works demonstrating the potential of deep learning-based thermal imaging as an objective method for the automated staging of pressure ulcers.