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Convolutional Neural Networks in Chronic Wound Segmentation and Tissue Classification Using Real-World Images.

Ellen Huttunen¹, Teija Kimpimäki^1,2, Jenni E Salenius¹

¹Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.

International Wound Journal

|April 16, 2026

Summary

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This summary is machine-generated.

Artificial intelligence (AI) can automatically segment chronic wound area and tissues from clinical images. A convolutional neural network achieved high accuracy for wound area segmentation, showing AI

Area of Science:

Medical imaging
Artificial intelligence in healthcare
Wound care technology

Background:

Chronic wounds represent a significant global health burden.
Objective diagnostic and monitoring tools are crucial for effective wound management.
Artificial intelligence presents a promising avenue for advancing wound care.

Purpose of the Study:

To train a convolutional neural network (CNN) for automatic segmentation of wound area and tissues from real-world clinical images.
To evaluate the performance of the AI model in identifying different wound components.

Main Methods:

Utilized a U-Net convolutional neural network architecture with fully supervised learning.
Trained the model on 362 real-world images of chronic wounds (venous, arterial, vasculitis, pyoderma gangrenosum).

Keywords:

artificial intelligence computer leg ulcer neural networks supervised machine learning wound healing

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Employed data augmentation and pretraining to enhance model performance.

Main Results:

Achieved high accuracy in wound area segmentation (Dice Similarity Coefficient [DSC] = 0.927, Intersection over Union [IoU] = 0.868).
Demonstrated fair performance for identifying fibrinous exudate (DSC = 0.750, IoU = 0.659) and granulation tissue (DSC = 0.696, IoU = 0.601).
Lower performance was observed for necrosis segmentation (DSC = 0.503, IoU = 0.502), likely due to limited image data.

Conclusions:

A neural network can be effectively trained to analyze clinical wound images for segmentation tasks.
AI models can identify various wound structures beyond just the total wound area.
Performance in identifying specific wound structures is contingent on the quantity of training data available for each structure.