Multi-modal wound classification using wound image and location by Xception and Gaussian Mixture Recurrent Neural Network (GMRNN)

TL;DR

This paper introduces a multi-modal AI model combining Xception and GMRNN architectures, utilizing transfer learning and location data to accurately classify various wound types, enhancing early diagnosis and treatment planning.

Contribution

The study presents a novel multi-modal deep learning approach integrating image and location data for wound classification, outperforming traditional neural networks in accuracy.

Findings

Achieved wound classification accuracy ranging from 78.77% to 100%.

Demonstrated the effectiveness of combining image features with location data.

Compared favorably against other deep neural networks in medical image analysis.

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.