WebFeb 25, 2024 · Raman spectroscopy is widely used as a fingerprint technique for molecular identification. However, Raman spectra contain molecular information from multiple components and interferences from noise and instrumentation. Thus, component identification using Raman spectra is still challenging, especially for mixtures. WebDecision letter for "An end‐to‐end deep learning approach for Raman spectroscopy classification" Nov 2024.
Deep Learning-Based Spectral Extraction for Improving
WebThe Raman spectroscopy analysis has been applied to the detection and research of oral cancer. One of the essential works in this technique is the Raman spectral data analysis method, which is mainly divided into two categories: traditional machine learning and deep learning. Especially, the deep learning method is proved that it could obtain ... WebRaman spectroscopy enables nondestructive, label-free imaging with unprecedented molecular contrast, but is limited by slow data acquisition, largely preventing high-throughput imaging applications. Here, we present a comprehensive framework for higher-throughput molecular imaging via deep-learning-enabled Raman spectroscopy, termed … speed breaker ahead sign
Deep learning methods for oral cancer detection using Raman spectroscopy
WebNov 22, 2024 · Herein we report on a deep-learning method for the removal of instrumental noise and unwanted spectral artifacts in Fourier transform infrared (FTIR) or Raman spectra, especially in automated applications in which a large number of spectra have to be acquired within limited time. WebAug 31, 2024 · Raman spectroscopy (RS) is a widely used analytical technique based on the detection of molecular vibrations in a defined system, which generates Raman spectra that contain unique and highly resolved fingerprints of the system. However, the low intensity of normal Raman scattering effect greatly hinders its application. WebReview for "An end‐to‐end deep learning approach for Raman spectroscopy classification" Oct 2024. speed breaker and potholes detection