Optimization of tissue classification for colorectal cancer detection using support vector machines and diffuse reflectance spectroscopy

2021 ◽  
Author(s):  
Marcelo Saito Nogueira ◽  
Michael Amissah ◽  
Siddra Maryam ◽  
Noel Lynch ◽  
Shane Killeen ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Support Vector Machines ◽  
Cancer Detection ◽  
Diffuse Reflectance ◽  
Diffuse Reflectance Spectroscopy ◽  
Reflectance Spectroscopy ◽  
Support Vector ◽  
Tissue Classification ◽  
Vector Machines

scholarly journals Evaluation of wavelength ranges and tissue depth probed by diffuse reflectance spectroscopy for colorectal cancer detection

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Marcelo Saito Nogueira ◽  
Siddra Maryam ◽  
Michael Amissah ◽  
Huihui Lu ◽  
Noel Lynch ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Diffuse Reflectance ◽  
Near Infrared ◽  
Diffuse Reflectance Spectroscopy ◽  
Ex Vivo ◽  
Reflectance Spectroscopy ◽  
Support Vector ◽  
And Performance ◽  
Diagnostic Capabilities ◽  
Invasive Techniques

AbstractColorectal cancer (CRC) is the third most common type of cancer worldwide and the second most deadly. Recent research efforts have focused on developing non-invasive techniques for CRC detection. In this study, we evaluated the diagnostic capabilities of diffuse reflectance spectroscopy (DRS) for CRC detection by building 6 classification models based on support vector machines (SVMs). Our dataset consists of 2889 diffuse reflectance spectra collected from freshly excised ex vivo tissues of 47 patients over wavelengths ranging from 350 and 1919 nm with source-detector distances of 630-µm and 2500-µm to probe different depths. Quadratic SVMs were used and performance was evaluated using twofold cross-validation on 10 iterations of randomized training and test sets. We achieved (93.5 ± 2.4)% sensitivity, (94.0 ± 1.7)% specificity AUC by probing the superficial colorectal tissue and (96.1 ± 1.8)% sensitivity, (95.7 ± 0.6)% specificity AUC by sampling deeper tissue layers. To the best of our knowledge, this is the first DRS study to investigate the potential of probing deeper tissue layers using larger SDD probes for CRC detection in the luminal wall. The data analysis showed that using a broader spectrum and longer near-infrared wavelengths can improve the diagnostic accuracy of CRC as well as probing deeper tissue layers.

scholarly journals Comparing in vivo and ex vivo fiberoptic diffuse reflectance spectroscopy in colorectal cancer

2019 ◽  
Vol 1 (1-2) ◽  
Author(s):  
Elisabeth J.M. Baltussen ◽  
Susan G. Brouwer De Koning ◽  
Benno H.W. Hendriks ◽  
Katarzyna Jóźwiak ◽  
Henricus J.C.M. Sterenborg ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Diffuse Reflectance ◽  
Diffuse Reflectance Spectroscopy ◽  
Ex Vivo ◽  
Reflectance Spectroscopy

scholarly journals Extended-wavelength diffuse reflectance spectroscopy with a machine-learning method for in vivo tissue classification

PLoS ONE ◽  
2019 ◽  
Vol 14 (10) ◽  
pp. e0223682 ◽  
Author(s):  
Ulf Dahlstrand ◽  
Rafi Sheikh ◽  
Cu Dybelius Ansson ◽  
Khashayar Memarzadeh ◽  
Nina Reistad ◽  
...  
Keyword(s):  
Machine Learning ◽  
Diffuse Reflectance ◽  
Diffuse Reflectance Spectroscopy ◽  
Reflectance Spectroscopy ◽  
Machine Learning Method ◽  
Learning Method ◽  
Tissue Classification
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