Inside Cover: Adaptive Boosting (AdaBoost)‐based multiwavelength spatial frequency domain imaging and characterization for ex vivo human colorectal tissue assessment | Zendy

Li Shuying | Zendy; Zeng Yifeng | Zendy; Chapman William C. | Zendy; Erfanzadeh Mohsen | Zendy; Nandy Sreyankar | Zendy; Mutch Matthew | Zendy; Zhu Quing | Zendy

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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 ).

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