Wavelength Selection of Near-Infrared Hyperspectral Imaging for Gastric Cancer Detection

• Main Authors: Yahata, Ayano, Takemura, Hiroshi, Takamatsu, Toshihiro, Yokota, Hideo, Iwanami, Ren, Umezawa, Masakazu, Okubo, Kyohei, Soga, Kohei, Mitsui, Tomohiro, Kadota, Tomohiro, Kuwata, Takeshi, Ikematsu, Hiroaki
• Format: Conference Proceeding
• Language: English
• Subjects: Biological system modeling; Cancer detection; Data models; Endoscopes; Gastric cancer; Near-infrared hyperspectral imaging; Real-time systems; Sensitivity; Supervised learning; Support vector machine; Support vector machines; The least absolute shrinkage and selection operator
• ISSN: 2189-8723
• DOI: 10.1109/ICIIBMS52876.2021.9651625

Gastric cancer is one of the most serious cancer that affects and kills many people around the world every year. Early treatment of gastric cancer dramatically improves the survival rate. Endoscopy has become an important tool for early detection. Since invasive gastric cancer or the edge of the invasive gastric cancer is difficult to find by using conventional visible-light endoscopy, near-infrared imaging, which is bringing great progress to the medical field, is focused on in recent years. In order to apply near-infrared hyperspectral imaging (NIR-HSI) in real-time, wavelength feature extraction is important because a large amount of data needs to be analyzed. The purpose of this study is to detect gastric cancer using NIR-HSI and to select a suitable wavelength for the target in the near-infrared region (1000 to 1600 nm). NIR-HSI was used to take data from six specimens of gastric cancer and each pixel was labeled as normal or tumor on the hyperspectral image based on the histopathological diagnosis. 4 wavelengths were extracted from 95 wavelengths using the least absolute shrinkage and selection operator method. Supervised learning was performed using a support vector machine for both cases using all 95 wavelengths and the case using 4 selected wavelengths. In both cases, the approximate location of the tumor could be identified and the appropriate wavelength could be selected. When all 95 wavelengths were used, the accuracy was 82.2 %, sensitivity was 93.0%, and specificity was 80.8%. When four wavelengths were used, the accuracy was 82.3%, the sensitivity was 96.8%, and the specificity was 82.7%. As a result, we found that even with 4 wavelengths, the classification was equivalent to using all 95 wavelengths. By applying the results of this study to the endoscope system, the possibility of constructing a NIR endoscope system for gastric cancer was suggested.