Outlier detection and removal improves accuracy of machine learning approach to multispectral burn diagnostic imaging

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

Research output: Contribution to journalArticle

34 Scopus citations

Abstract

Multispectral imaging (MSI) was implemented to develop a burn tissue classification device to assist burn surgeons in planning and performing debridement surgery. To build a classification model via machine learning, training data accurately representing the burn tissue was needed, but assigning raw MSI data to appropriate tissue classes is prone to error. We hypothesized that removing outliers from the training dataset would improve classification accuracy. A swine burn model was developed to build an MSI training database and study an algorithm's burn tissue classification abilities. After the ground-truth database was generated, we developed a multistage method based on Z-test and univariate analysis to detect and remove outliers from the training dataset. Using 10-fold cross validation, we compared the algorithm's accuracy when trained with and without the presence of outliers. The outlier detection and removal method reduced the variance of the training data. Test accuracy was improved from 63% to 76%, matching the accuracy of clinical judgment of expert burn surgeons, the current gold standard in burn injury assessment. 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)
Article number121305
JournalJournal of Biomedical Optics
Volume20
Issue number12
DOIs
StatePublished - Dec 1 2015

Keywords

  • burn
  • machine learning
  • medical imaging
  • model accuracy
  • multispectral imaging
  • outlier detection

ASJC Scopus subject areas

  • Biomedical Engineering
  • Biomaterials
  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics

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    Li, W., Mo, W., Zhang, X., Squiers, J. J., Lu, Y., Sellke, E. W., Fan, W., Dimaio, J. M., & Thatcher, J. E. (2015). Outlier detection and removal improves accuracy of machine learning approach to multispectral burn diagnostic imaging. Journal of Biomedical Optics, 20(12), [121305]. https://doi.org/10.1117/1.JBO.20.12.121305