3D Hyperspectral Data Analysis with Spatially Aware Deep Learning for Diagnostic Applications.

ABSTRACT: Nowadays, with the rise of artificial intelligence (AI), deep learning algorithms play an increasingly important role in various traditional fields of research. Recently, these algorithms have already spread into data analysis for Raman spectroscopy. However, most current methods only use 1-dimensional (1D) spectral data classification, instead of considering any neighboring information in space. Despite some successes, this type of methods wastes the 3-dimensional (3D) structure of Raman hyperspectral scans. Therefore, to investigate the feasibility of preserving the spatial information on Raman spectroscopy for data analysis, spatially aware deep learning algorithms were applied into a colorectal tissue data set with 3D Raman hyperspectral scans. This data set contains Raman spectra from

SUBMITTER: Luo R 

PROVIDER: S-EPMC12004353 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature