TY - JOUR AU - Akimoto, Kohei AU - Ike, Reiichirou AU - Maeda, Kosuke AU - Hosokawa, Naoki AU - Takamatsu, Toshihiro AU - Soga, Kohei AU - Yokota, Hideo AU - Sato, Daiki AU - Kuwata, Takeshi AU - Ikematsu, Hiroaki AU - Takemura, Hiroshi PY - 2021/02/10 Y2 - 2024/03/29 TI - Wavelength Bands Reduction Method in Near-Infrared Hyperspectral Image based on Deep Neural Network for Tumor Lesion Classification JF - European Journal of Applied Sciences JA - EJAS VL - 9 IS - 1 SE - Articles DO - 10.14738/aivp.91.9475 UR - https://journals.scholarpublishing.org/index.php/AIVP/article/view/9475 SP - 273-281 AB - <p>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.</p> ER -