Current Optics and Photonics

Optical Society of Korea (한국광학회)
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Classification of Midinfrared Spectra of Colon Cancer Tissue Using a Convolutional Neural Network

  • Kim, In Gyoung (Medical & Bio Photonics Research Center, Korea Photonics Technology Institute) ;
  • Lee, Changho (School of Dentistry, Chonnam National University) ;
  • Kim, Hyeon Sik (Medical & Bio Photonics Research Center, Korea Photonics Technology Institute) ;
  • Lim, Sung Chul (Department of Pathology, Chosun University Hospital) ;
  • Ahn, Jae Sung (Medical & Bio Photonics Research Center, Korea Photonics Technology Institute)
  • Received : 2021.09.16
  • Accepted : 2021.11.17
  • Published : 2022.02.25
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Abstract

The development of midinfrared (mid-IR) quantum cascade lasers (QCLs) has enabled rapid high-contrast measurement of the mid-IR spectra of biological tissues. Several studies have compared the differences between the mid-IR spectra of colon cancer and noncancerous colon tissues. Most mid-IR spectrum classification studies have been proposed as machine-learning-based algorithms, but this results in deviations depending on the initial data and threshold values. We aim to develop a process for classifying colon cancer and noncancerous colon tissues through a deep-learning-based convolutional-neural-network (CNN) model. First, we image the midinfrared spectrum for the CNN model, an image-based deep-learning (DL) algorithm. Then, it is trained with the CNN algorithm and the classification ratio is evaluated using the test data. When the tissue microarray (TMA) and routine pathological slide are tested, the ML-based support-vector-machine (SVM) model produces biased results, whereas we confirm that the CNN model classifies colon cancer and noncancerous colon tissues. These results demonstrate that the CNN model using midinfrared-spectrum images is effective at classifying colon cancer tissue and noncancerous colon tissue, and not only submillimeter-sized TMA but also routine colon cancer tissue samples a few tens of millimeters in size.

Keywords

Acknowledgement

This work was supported by the Korean Medical Device Development Fund grant funded by the Korean government (the Ministry of Science and ICT, the Ministry of Trade, Industry and Energy, the Ministry of Health & Welfare, and the Ministry of Food and Drug Safety) (Project Number: 1711137874, KMDF_PR_20200901_0008), and was also partially supported by a grant from the "HPC Support" Project, supported by the "Ministry of Science and ICT" and NIPA.

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