Beyond single views: leveraging input-level diversity for explainable and robust chronic wound classification

Chronic wound classification remains a major challenge in clinical settings due to the subjective nature of visual assessment, inconsistent documentation practices, and the presence of overlapping visual features across wound types. These limitations contribute to delayed or inaccurate diagnosis, which in turn compromises treatment outcomes. Despite the growing use of deep learning in medical imaging, most existing models in wound analysis rely on single-view input, limiting their ability to generalise under real-world clinical conditions. In this study, we present a deep learning-based framework that leverages input-level diversity through ensemble and multi-branch convolutional neural networks (CNNs). Our approach uses three image variants: original, Lab colour space contrast limited adaptive histogram equalisation (CLAHE), and maximum green greyscale channel CLAHE —processed through identical pre-trained CNNs. We conducted experiments on a combined wound image dataset drawn from the publicly available advancing the Zenith of Healthcare dataset and an independent proprietary dataset, as well as external validation using the Medetec wound image database. Our Input-diverse ensemble framework, based on weighted probability fusion achieved an accuracy of 89.23% and F 1-score of 82.95%, outperforming individual model variants and demonstrating strong generalisation during external validation. Comparative analysis also shows that our model meets and exceeds the performance of existing state-of-the-art methods reported on similar classification tasks. The proposed method introduces a lightweight yet effective solution for improving classification consistency across multiple wound types without requiring complex multimodal data or additional clinical metadata. Its design offers a practical step forward for integrating deep learning into clinical workflows, enabling scalable, interpretable, and more reliable wound assessment tools for frontline healthcare professionals.