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Spectroscopy and Machine Learning: Development of Methods for Cancer Detection Using Mid-Infrared Wavelengths

Bradley, Rebecca C
http://rave.ohiolink.edu/etdc/view?acc_num=osu1620658484449199

Abstract Details

2021, Doctor of Philosophy, Ohio State University, Chemical Physics.
Cancer is a disease that affects millions of people each year, and cancer detection is currently done using costly and inefficient methods. The purpose of this research has been to develop methods that use infrared spectroscopy and machine learning to accurately and efficiently detect cancer. The vibrational information from the molecules of tissue can be accessed through infrared spectroscopy and various spectral metrics including those from spectral peak ratios, calibrant spectra, and principal component analysis of spectral libraries. This information is coupled with machine learning methods for separation and feature selection. Using these methods, two imaging experiments were conducted on SKH-1 mice with skin cancer and colorectal cancer metastatic to the liver in humans. Support vector machine learning methods were able to separate the tumor spectra from other spectra, including nontumor, with high accuracy. Support vector machines were also used to determine optimum peak ratios for separation to reduce the number of wavelengths needed. Support vector machine methods were also compared with metrics from currently used tissue staining techniques – hematoxylin and eosin – which showed that infrared spectra are more effective at separating cancer under the present conditions. These known optical techniques were also joined with infrared spectroscopy for a combined approach. Using the support vector machine decision equation, images of tissue were created to aid in the diagnosis of cancer. The methods developed were used as a basis for the design of a fast infrared probe that can detect skin cancer with high levels of accuracy in a clinical trial. The fast infrared probe was also able to separate between two different types of skin cancer – basal and squamous cell carcinomas. This prototype probe could be modified with an etalon filter to increase the efficiency of the probe when used in a clinical setting. This research develops and tests methods that show that infrared spectroscopy can be combined with machine learning algorithms to accurately and effectively detect cancer.
James Coe, Ph.D. (Advisor)
Heather Allen, Ph.D. (Committee Member)
Sherwin Singer, Ph.D. (Committee Member)
Dongping Zhong, Ph.D. (Committee Member)
287 p.
Chemistry; Physics
infrared spectroscopy, support vector machines, machine learning, cancer detection, basal cell carcinoma, squamous cell carcinoma, attenuated total reflectance, colorectal cancer metastatic to the liver, transfer matrix method, cavity-vibration polaritons

Recommended Citations

Citations

  • Bradley, R. C. (2021). Spectroscopy and Machine Learning: Development of Methods for Cancer Detection Using Mid-Infrared Wavelengths [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1620658484449199

    APA Style (7th edition)

  • Bradley, Rebecca. Spectroscopy and Machine Learning: Development of Methods for Cancer Detection Using Mid-Infrared Wavelengths. 2021. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1620658484449199.

    MLA Style (8th edition)

  • Bradley, Rebecca. "Spectroscopy and Machine Learning: Development of Methods for Cancer Detection Using Mid-Infrared Wavelengths." Doctoral dissertation, Ohio State University, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1620658484449199

    Chicago Manual of Style (17th edition)

osu1620658484449199
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This open access ETD is published by The Ohio State University and OhioLINK.