Multi-modal wound classification using wound image and location by Xception and Gaussian Mixture Recurrent Neural Network (GMRNN)
Journal:
arXiv
Published Date:
May 12, 2025
Abstract
The effective diagnosis of acute and hard-to-heal wounds is crucial for wound
care practitioners to provide effective patient care. Poor clinical outcomes
are often linked to infection, peripheral vascular disease, and increasing
wound depth, which collectively exacerbate these comorbidities. However,
diagnostic tools based on Artificial Intelligence (AI) speed up the
interpretation of medical images and improve early detection of disease. In
this article, we propose a multi-modal AI model based on transfer learning
(TL), which combines two state-of-the-art architectures, Xception and GMRNN,
for wound classification. The multi-modal network is developed by concatenating
the features extracted by a transfer learning algorithm and location features
to classify the wound types of diabetic, pressure, surgical, and venous ulcers.
The proposed method is comprehensively compared with deep neural networks (DNN)
for medical image analysis. The experimental results demonstrate a notable
wound-class classifications (containing only diabetic, pressure, surgical, and
venous) vary from 78.77 to 100\% in various experiments. The results presented
in this study showcase the exceptional accuracy of the proposed methodology in
accurately classifying the most commonly occurring wound types using wound
images and their corresponding locations.
