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

doi:10.1117/1.JBO.20.12.121305

25 August 2015 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

Author Affiliations +

Journal of Biomedical Optics, Vol. 20, Issue 12, 121305 (August 2015). https://doi.org/10.1117/1.JBO.20.12.121305

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.

Li, Mo, Zhang, Squiers, Lu, Sellke, Fan, DiMaio, and Thatcher: Outlier detection and removal improves accuracy of machine learning approach to multispectral burn diagnostic imaging

Citation Download Citation

Weizhi Li, Weirong Mo, Xu Zhang, John J. Squiers, Yang Lu, Eric W. Sellke, Wensheng Fan, J. Michael DiMaio, and Jeffrey E. Thatcher "Outlier detection and removal improves accuracy of machine learning approach to multispectral burn diagnostic imaging," Journal of Biomedical Optics 20(12), 121305 (25 August 2015). https://doi.org/10.1117/1.JBO.20.12.121305

Published: 25 August 2015

Access the abstract

JOURNAL ARTICLE
9 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY