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Inside Cover: Adaptive Boosting (AdaBoost)‐based multiwavelength spatial frequency domain imaging and characterization for ex vivo human colorectal tissue assessment
Author(s) -
Li Shuying,
Zeng Yifeng,
Chapman William C.,
Erfanzadeh Mohsen,
Nandy Sreyankar,
Mutch Matthew,
Zhu Quing
Publication year - 2020
Publication title -
journal of biophotonics
Language(s) - English
Resource type - Reports
SCImago Journal Rank - 0.877
H-Index - 66
eISSN - 1864-0648
pISSN - 1864-063X
DOI - 10.1002/jbio.202070016
Subject(s) - adaboost , boosting (machine learning) , artificial intelligence , ex vivo , computer science , colorectal cancer , pattern recognition (psychology) , computer vision , medicine , in vivo , biology , cancer , support vector machine , microbiology and biotechnology
A multi‐wavelength Spatial Frequency Domain Imaging (SFDI) utilizes structured illumination to provide absorption and reduced scattering coefficient maps of colorectal tissue. Combining SFDI with a Machine Learning algorithm ‐ AdaBoost, different types of colorectal tissues including normal, adenomatous polyp and cancer, can be differentiated with high accuracy. This new technique provides a potential method to assist in colorectal cancer screening. Further details can be found in the article by Shuying Li, Yifeng Zeng, William C. Chapman Jr, et al. ( e201960241 ).