Convolutional neural network-based retrieval of Raman signals from CARS spectra

Abstract

We report the studies on the automatic extraction of the Raman signal from coherent anti-Stokes Raman scattering (CARS) spectra by using a convolutional neural network (CNN) model. The model architecture is adapted from literature and retrained with synthetic and semi-synthetic data. The synthesized CARS spectra better approximate the experimental CARS spectra. The retrained model accurately predicts spectral lines throughout the spectral range, even with minute intensities, which demonstrates the potential of the model. Further, the extracted Raman line-shapes are in good agreement with the original ones, with an RMS error of less than 7% on average and have shown correlation coefficients of more than 0.9. Finally, this approach has a strong potential in accurately estimating Raman signals from complex CARS data for various applications.

Effect of non-resonant background on the extraction of Raman signals from CARS spectra using deep neural networks

“… 35 In our recent work, we have also demonstrated that the training with semi-synthetic data in addition to the synthetic data improves the DL model performance. 36 The SpecNet model performance was found to be poor when compared with our previous work 36 and VECTOR model, 35 where it could not able to extract the spectral lines with minimal intensities. More importantly, they have not evaluated the model accuracy quantitatively.…”

“…A similar observation was noticed in our previous work. 36 Further, this inefficient extraction of the Raman spectrum has given an SE of ∼0.29 at the peak centre and led to the lowest PCC in the entire test set, i.e. , ∼0.11.…”

“…A similar observation was noticed in our previous work. 36 Further, this inefficient extraction of the Raman spectrum has given an SE of $0.29 at the peak centre and led to the lowest PCC in the entire test set, i.e., $0.11. The other two models have predicted the Raman line, albeit the extracted intensity is low compared to the actual one.…”

“…[30][31][32] In recent times, these DL algorithms have also been deployed to solve the issue of NRB in CARS measurements. [33][34][35][36] Houhou et al have utilized the Long Short-Term Memory (LSTM) model to extract the Raman signal and compared their results with the KK & MEM computational techniques. 33 Valensise et al employed a convolutional neural network (CNN) model for retrieving the imaginary part of CARS spectra.…”

“…Thus, all investigations reported to date have trained their DL model only by considering the same type of NRB irrespective of their DL model architecture, and no other type is explored. [34][35][36] Even the recent VECTOR model has shown that robust performance is merely achieved for the simulated datasets, whereas it is found to be sensitive when dealing with the actual experimental data. It is attributed to the fact that NRB used in that study is simply modelled as a product of the countervailing sigmoids and hinted that further improvements on NRB are required to handle complex CARS data.…”