BayesOpGAN: A Bayesian-Optimized GAN Framework with Fourier-Based Evaluation for Quality-Controlled Raman Spectral...

Area of Science:

• Spectroscopy
• Machine Learning
• Data Augmentation

Background:

• Deep learning models for Raman spectral classification struggle with small datasets, leading to overfitting and poor generalization.
• Generating synthetic spectral data is crucial for improving model performance with limited samples.

Purpose of the Study:

• To introduce BayesOpGAN, a novel generative adversarial network for creating high-fidelity Raman spectra from minimal data (<30 samples).
• To quantitatively assess the quality of generated spectra using Fourier Distance, a frequency-domain metric.
• To demonstrate the effectiveness of BayesOpGAN-augmented data in enhancing deep learning classifier performance.

Main Methods:

• Development of BayesOpGAN, a generative adversarial network featuring a Bayesian-optimized loss (BayesOpLoss) and smooth upsampling modules.
• Quantitative spectral quality assessment using Fourier Distance, a metric for one-dimensional signals.
• Validation on a hybrid-quality dataset from the RRUFF Raman database using a ResNet-50 classifier.

Main Results:

• BayesOpGAN successfully generated fidelity Raman spectra from fewer than 30 samples.
• Fourier Distance provided an objective measure of spectral quality.
• Augmentation with BayesOpGAN-generated data improved ResNet-50 classifier test accuracy from 83.9% to 91.0%.
• Excessive data augmentation was found to reduce performance, highlighting an optimal augmentation range.

Conclusions:

• BayesOpGAN offers a reliable and scalable solution for data augmentation in small-sample spectral classification.
• Fourier Distance is a suitable metric for evaluating the fidelity of generated one-dimensional spectral data.
• The BayesOpGAN framework shows potential for generalization to other one-dimensional spectroscopies and biomedical sensing applications.