Poster + Paper
3 April 2024 Detection of deep lesion in resected stomach by near-infrared hyperspectral imaging
Toshihiro Takamatsu, Ryodai Fukushima, Hideo Yokota, Hiroaki Ikematsu, Kohei Soga, Hiroshi Takemura
Author Affiliations +
Conference Poster
Abstract
Near-infrared hyperspectral imaging (NIR-HSI) is well known that it enables chemical composition analysis with high bio-transparency and high spatial resolution. Thus, hyperspectral imaging is potential in noninvasive and label-free diagnosis of deep lesion by machine learning. In this study, detection of deep lesions such as Gastrointestinal Stromal Tumor (GIST) and Gastric Cancer (GC) including unexposed areas was investigated using NIR-HSI. As the result, although GIST specimens had a normal mucosal layer covering the lesion, NIR-HSI analysis by machine learning showed an average prediction accuracy of 86.1%. In case of GC specimens, average prediction accuracy of GC regions in all area, exposed area and unexposed area were 79.9%, 80.9% and 77.8%, respectively.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Toshihiro Takamatsu, Ryodai Fukushima, Hideo Yokota, Hiroaki Ikematsu, Kohei Soga, and Hiroshi Takemura "Detection of deep lesion in resected stomach by near-infrared hyperspectral imaging", Proc. SPIE 12927, Medical Imaging 2024: Computer-Aided Diagnosis, 129271R (3 April 2024); https://doi.org/10.1117/12.3006359
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Cancer

Cancer detection

Hyperspectral imaging

Machine learning

Resection

Stomach

Endoscopy

RELATED CONTENT


Back to Top