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Convolution neural network for effective burn region segmentation of color images.

Background: Burn injuries are one of the most severe forms of wounds and trauma across the globe. Automated burn diagnosis methods are needed to provide timely treatment to the concerned patients. Artificial intelligence is playing a vital role in developing automated tools and techniques for medica...

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Published in:Burns (03054179) Vol. 47; no. 4; pp. 854 - 863
Main Authors: Chauhan, Joohi, Goyal, Puneet
Format: Journal Article
Published: Elsevier B.V. Jun2021
Online Access:View this record in EBSCOhost
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      dt: Jun2021
      vid: 47
      iid: 4
      pid: 82545
      pub: Elsevier B.V.
      place: Philadelphia, Pennsylvania
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        150850496
        150850496
        NLM33158632
        10.1016/j.burns.2020.08.016
        NLM33158632
        150850496
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        atl: Convolution neural network for effective burn region segmentation of color images.
      aug:
        au:
          Chauhan, Joohi
          Goyal, Puneet
        affil: Center for Biomedical Engineering, Indian Institute of Technology Ropar, Punjab, India
      sug:
        subj:
          Burns
          Image Processing, Computer Assisted Standards
          Image Processing, Computer Assisted
          Image Processing, Computer Assisted Methods
          Scales
      ab: Background: Burn injuries are one of the most severe forms of wounds and trauma across the globe. Automated burn diagnosis methods are needed to provide timely treatment to the concerned patients. Artificial intelligence is playing a vital role in developing automated tools and techniques for medical problems. However, the use of advanced AI techniques for color images based burn region segmentation is not much explored.Method: In this work, we explore the use of deep learning for the challenging problem of burn region segmentation. We prepared a pixel-wise labelled new burn images dataset for segmentation and investigated the efficacy of existing state-of-the-art color images based semantic image segmentation techniques. Lately, we proposed a new convolution neural network (CNN) that uses atrous convolution for encoding rich contextual information and utilizes pre-trained model ResNet-101 for better extraction of low-level and middle-level layer features.Results: The proposed approach achieves the state-of-the-art performance on the prepared burn image dataset with 77.6% of Mathews correlation coefficient (MCC) and 93.4% of accuracy. The improvement of 11.6/5.8/6.9/1.2% is observed in precision, Dice similarity coefficient, Jaccard index and specificity, in comparison to the second best performance.Conclusion: In this work, we propose a CNN based novel method for performing burn-region segmentation in color images and evaluate it using newly prepared Burn Images dataset. The experimental results illustrate its effectiveness in comparison to existing approaches. Further, the proposed pixel-level segmentation method could be useful in estimating the burn surface area and burn severity in an accurate and time efficient manner.
      pubtype: Academic Journal
      doctype: Journal Article
      ougenre: Article
    language: English
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