Wavelength Bands Reduction Method in Near-Infrared Hyperspectral Image based on Deep Neural Network for Tumor Lesion Classification


  • Kohei Akimoto Tokyo University of Science
  • Reiichirou Ike Faculty of Science and Technology, Tokyo University of Science, Japan;
  • Kosuke Maeda Faculty of Science and Technology, Tokyo University of Science, Japan;
  • Naoki Hosokawa Faculty of Science and Technology, Tokyo University of Science, Japan;
  • Toshihiro Takamatsu Faculty of Science and Technology, Tokyo University of Science, Japan;
  • Kohei Soga Faculty of Industrial Science and Technology, Tokyo University of Science, Japan;
  • Hideo Yokota RIKEN Center for Advanced Photonics, Japan
  • Daiki Sato 2National Cancer Center Hospital East, Japan;
  • Takeshi Kuwata National Cancer Center Hospital East, Japan
  • Hiroaki Ikematsu National Cancer Center Hospital East, Japan;
  • Hiroshi Takemura Faculty of Science and Technology, Tokyo University of Science, Japan;




Near-infrared, Hyperspectral imaging, Machine learning, Wavelength reduction


In this paper, we propose a method for wavelength bands reduction of near-infrared (NIR) hyperspectral imaging data to extract the cancer region with minimum input data. NIR hyperspectral imaging data has a spectrum data of each pixel and is suitable for distinguishing tumors region of the body rather than RGB imaging data. However, it is difficult to applicate to the medical field because of processing time consumption and hardware size limitation. Therefore, it is necessary to remove the redundant wavelength bands which are not (or little) contributed to tumor region extraction. Although several previous studies for wavelength bands reduction have been conducted, these approaches focused on the characteristics of the wavelength itself. In this research, the proposed wavelength bands reduction method is focused on the node weights of the post-training deep neural network which is an indicator directly related to classification. The experimental results using GIST specimen demonstrated that four wavelength bands selected from all wavelengths bands by using the proposed method are effective for tumor distinguishing as well as all wavelength bands.


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How to Cite

Akimoto, K., Ike, R. ., Maeda, K. ., Hosokawa, N. ., Takamatsu, T. ., Soga, K. ., Yokota, H. ., Sato, D. ., Kuwata, T. ., Ikematsu, H. ., & Takemura, H. . (2021). Wavelength Bands Reduction Method in Near-Infrared Hyperspectral Image based on Deep Neural Network for Tumor Lesion Classification. European Journal of Applied Sciences, 9(1), 273–281. https://doi.org/10.14738/aivp.91.9475