Baseline correction of Raman spectra using static dropout triangular deep convolutional network

Raman spectroscopy has attracted much attention due to its wide applications in drug detection and many other fields. However, Raman spectra often contain background noise, which poses significant challenges for subsequent analysis and processing. Although various methods for removing background noise have been proposed and have improved analysis accuracy to some extent, these mathematical model-based methods often rely on parameter adjustments based on spectral data to achieve the desired de-noising effect. The introduction of deep learning technology has provided new ideas to solve this problem, breaking through the limitations of traditional methods in parameter dependency. Different deep learning structures exhibit unique advantages when processing different types of data. In this study, we propose a static dropout triangular deep convolutional network (SD-TDCN). This deep learning network can maintain superior performance while significantly reducing the size of model parameters by statically discarding some convolutional blocks in the deep learning network. In addition, this deep learning network also lays an experimental foundation for the subsequent development of deep learning structures with adaptive dropout mechanisms.