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Journal of Biomedical Optics

Outlier detection and removal improves accuracy of machine learning approach to multispectral burn diagnostic imaging
Author(s): Weizhi Li; Weirong Mo; Xu Zhang; John J. Squiers; Yang Lu; Eric W. Sellke; Wensheng Fan; J. Michael DiMaio; Jeffrey E. Thatcher
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Paper 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.

Paper Details

Date Published: 25 August 2015
PDF: 9 pages
J. Biomed. Opt. 20(12) 121305 doi: 10.1117/1.JBO.20.12.121305
Published in: Journal of Biomedical Optics Volume 20, Issue 12
Show Author Affiliations
Weizhi Li, Spectral MD, Inc. (United States)
Weirong Mo, Spectral MD, Inc. (United States)
Xu Zhang, Spectral MD, Inc. (United States)
John J. Squiers, Spectral MD, Inc. (United States)
Baylor Research Institute (United States)
Yang Lu, Spectral MD, Inc. (United States)
Eric W. Sellke, Spectral MD, Inc. (United States)
Wensheng Fan, Spectral MD, Inc. (United States)
J. Michael DiMaio, Spectral MD, Inc. (United States)
Baylor Research Institute (United States)
Jeffrey E. Thatcher, Spectral MD, Inc. (United States)

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