Scholarlink

<p>The global burden of acute and chronic wounds continues to demand improved classification methods, a critical step in guiding appropriate treatment plans. In this work, we present a lightweight dual-backbone deep learning architecture that combines EfficientNet-B3 and MobileNetV3-Small in a parallel feature extraction framework for wound image classification. The proposed network employs depthwise separable reduction layers, dynamic multi-scale mixers, parallel squeeze-and-excitation attention modules, efficient channel attention blocks, and a learned gated fusion mechanism to merge complementary features from both backbones. Our model was trained and evaluated on two publicly available datasets (AZH and Medetec) across whole image and Region of Interest (ROI) classification protocols using a 70/15/15 train-validation-test split, with all metrics reported as mean ± standard deviation across 10 independent trials. On the four-class whole image AZH benchmark, the proposed model achieves 84.46 ± 2.30% accuracy, improving on the previous best of 83.13%. On the six-class ROI task it reaches 85.83 ± 0.59% accuracy, competitive with prior work while using image data alone without requiring wound location metadata. Grad-CAM visualizations confirm that the network focuses on clinically relevant wound regions, supporting its potential as a decision-support tool for wound care practitioners.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Lightweight dual-backbone network with attentional fusion for wound image classification

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月1日

查看全文
  • Dev Patel,
  • Aiken Bekbolat,
  • Tim Brosi,
  • Zeyun Yu,
  • Janelle Elias,
  • Yash Patel

摘要

The global burden of acute and chronic wounds continues to demand improved classification methods, a critical step in guiding appropriate treatment plans. In this work, we present a lightweight dual-backbone deep learning architecture that combines EfficientNet-B3 and MobileNetV3-Small in a parallel feature extraction framework for wound image classification. The proposed network employs depthwise separable reduction layers, dynamic multi-scale mixers, parallel squeeze-and-excitation attention modules, efficient channel attention blocks, and a learned gated fusion mechanism to merge complementary features from both backbones. Our model was trained and evaluated on two publicly available datasets (AZH and Medetec) across whole image and Region of Interest (ROI) classification protocols using a 70/15/15 train-validation-test split, with all metrics reported as mean ± standard deviation across 10 independent trials. On the four-class whole image AZH benchmark, the proposed model achieves 84.46 ± 2.30% accuracy, improving on the previous best of 83.13%. On the six-class ROI task it reaches 85.83 ± 0.59% accuracy, competitive with prior work while using image data alone without requiring wound location metadata. Grad-CAM visualizations confirm that the network focuses on clinically relevant wound regions, supporting its potential as a decision-support tool for wound care practitioners.