Deep Learning Method to Predict Wound Healing Progress Based on Collagen Fibers in Wound Tissue

TL;DR

This paper introduces a deep learning approach using transfer learning and LayerCAM to classify wound healing stages based on collagen fiber features in histological images, achieving high accuracy and interpretability.

Contribution

It presents the first deep learning model for predicting wound healing stages from collagen fiber images, utilizing transfer learning and visualization for transparency.

Findings

Achieved 82% accuracy in classifying six wound healing stages

Utilized transfer learning with VGG16 pretrained on ImageNet

Enhanced model interpretability with LayerCAM visualization

Abstract

Wound healing is a complex process involving changes in collagen fibers. Accurate monitoring of these changes is crucial for assessing the progress of wound healing and has significant implications for guiding clinical treatment strategies and drug screening. However, traditional quantitative analysis methods focus on spatial characteristics such as collagen fiber alignment and variance, lacking threshold standards to differentiate between different stages of wound healing. To address this issue, we propose an innovative approach based on deep learning to predict the progression of wound healing by analyzing collagen fiber features in histological images of wound tissue. Leveraging the unique learning capabilities of deep learning models, our approach captures the feature variations of collagen fibers in histological images from different categories and classifies them into various stages of wound healing. To overcome the limited availability of histological image data, we employ a transfer learning strategy. Specifically, we fine-tune a VGG16 model pretrained on the ImageNet dataset to adapt it to the classification task of histological images of wounds. Through this process, our model achieves 82% accuracy in classifying six stages of wound healing. Furthermore, to enhance the interpretability of the model, we employ a class activation mapping technique called LayerCAM. LayerCAM reveals the image regions on which the model relies when making predictions, providing transparency to the model's decision-making process. This visualization not only helps us understand how the model identifies and evaluates collagen fiber features but also enhances trust in the model's prediction results. To the best of our knowledge, our proposed model is the first deep learning-based classification model used for predicting wound healing stages.