Multispectral imaging burn wound tissue classification system: A comparison of test accuracies between several common machine learning algorithms

John J. Squiers, Weizhi Li, Darlene R. King, Weirong Mo, Xu Zhang, Yang Lu, Eric W. Sellke, Wensheng Fan, J. Michael DiMaio, Jeffrey E. Thatcher

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations


The clinical judgment of expert burn surgeons is currently the standard on which diagnostic and therapeutic decisionmaking regarding burn injuries is based. Multispectral imaging (MSI) has the potential to increase the accuracy of burn depth assessment and the intraoperative identification of viable wound bed during surgical debridement of burn injuries. A highly accurate classification model must be developed using machine-learning techniques in order to translate MSI data into clinically-relevant information. An animal burn model was developed to build an MSI training database and to study the burn tissue classification ability of several models trained via common machine-learning algorithms. The algorithms tested, from least to most complex, were: K-nearest neighbors (KNN), decision tree (DT), linear discriminant analysis (LDA), weighted linear discriminant analysis (W-LDA), quadratic discriminant analysis (QDA), ensemble linear discriminant analysis (EN-LDA), ensemble K-nearest neighbors (EN-KNN), and ensemble decision tree (EN-DT). After the ground-truth database of six tissue types (healthy skin, wound bed, blood, hyperemia, partial injury, full injury) was generated by histopathological analysis, we used 10-fold cross validation to compare the algorithms' performances based on their accuracies in classifying data against the ground truth, and each algorithm was tested 100 times. The mean test accuracy of the algorithms were KNN 68.3%, DT 61.5%, LDA 70.5%, W-LDA 68.1%, QDA 68.9%, EN-LDA 56.8%, EN-KNN 49.7%, and EN-DT 36.5%. LDA had the highest test accuracy, reflecting the bias-variance tradeoff over the range of complexities inherent to the algorithms tested. Several algorithms were able to match the current standard in burn tissue classification, the clinical judgment of expert burn surgeons. These results will guide further development of an MSI burn tissue classification system. Given that there are few surgeons and facilities specializing in burn care, this technology may improve the standard of burn care for patients without access to specialized facilities.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2016
Subtitle of host publicationComputer-Aided Diagnosis
EditorsGeorgia D. Tourassi, Samuel G. Armato
ISBN (Electronic)9781510600201
StatePublished - 2016
EventMedical Imaging 2016: Computer-Aided Diagnosis - San Diego, United States
Duration: Feb 28 2016Mar 2 2016

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


OtherMedical Imaging 2016: Computer-Aided Diagnosis
Country/TerritoryUnited States
CitySan Diego


  • Algorithm development
  • Burns
  • Machine learning
  • Multispectral imaging
  • Tissue classification

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging
  • Biomaterials


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