Machine learning has had a significant impact on the value of spectroscopic characterization tools, particularly in biomedical applications, due to its ability to detect latent patterns within complex spectral data. However, it often requires extensive data preprocessing, including baseline correction and denoising, which can lead to an unintentional bias during classification. To address this, we developed two deep learning methods capable of fully preprocessing raw Raman spectroscopy data without any human input. First, cascaded deep convolutional neural networks (CNN) based on either ResNet or U-Net architectures were trained on randomly generated spectra with augmented defects. Then, they were tested using simulated Raman spectra, surface-enhanced Raman spectroscopy (SERS) imaging of chemical species, low resolution Raman spectra of human bladder cancer tissue, and finally, classification of SERS spectra from human placental extracellular vesicles (EVs). Both approaches resulted in faster training and complete spectral preprocessing in a single step, with more speed, defect tolerance, and classification accuracy compared to conventional methods. These findings indicate that cascaded CNN preprocessing is ideal for biomedical Raman spectroscopy applications in which large numbers of heterogeneous spectra with diverse defects need to be automatically, rapidly, and reproducibly preprocessed.

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A three-dimensional transformation optics method, leading to homogeneous materials, applicable to any non-Cartesian coordinate systems or waveguides/objects of arbitrary cross-sections is presented. Both the conductive boundary and internal material of the desired device is determined by the proposed formulation. The method is applicable to a wide range of waveguide, radiation, and cloaking problems, and is demonstrated for circular waveguide couplers and an external cloak. An advantage of the present method is that the material properties are simplified by appropriately selecting the conductive boundaries. For instance, a right-angle circular waveguide bend is presented which uses only one homogenous material. Also, transformation of conductive materials and boundaries are studied. The conditions in which the transformed boundaries remain conductive are discussed. In addition, it is demonstrated that negative infinite conductivity can be replaced with positive conductivity, without affecting the field outside the conductive boundary. It is also observed that a negative finite conductivity can be replaced with a positive one, by accepting some small errors. The general mathematical procedure and formulation for calculating the parametric surface equations of the conductive peripheries are presented.

Machine learning has had a significant impact on the value of spectroscopy-based characterization tools, particularly in biomedical applications, due to its ability to detect latent patterns within complex spectral data. However, it often requires extensive data preprocessing, including baseline correction and denoising, which can lead to unintentional bias during classification. To address this, we present a deep learning-based signal preprocessing method capable of handling all the defects of raw Raman spectroscopy data without any need of human intervention. To achieve this, a novel deep convolutional neural network (CNN) architecture was trained on randomly generated spectra with various defects. We demonstrate that the proposed network results in faster training and that it can perform complete spectral preprocessing in a single step with more accuracy, speed, and defect tolerance than conventional methods. These improvements make it an ideal candidate for hyperspectral imaging applications in which tens of thousands of raw spectra may need to be processed rapidly. The superiority of this method is demonstrated for simulated Raman spectra, surface-enhanced Raman spectroscopy (SERS) imaging of chemical species, classification of low resolution Raman spectra of human bladder cancer tissue, and finally classification of SERS spectra from human placental extracellular vesicles (EVs). These findings encourage the future use of deep learning as a rapid and unbiased method of preprocessing spectroscopy data and may be particularly useful in biomedical applications involving large data sets from highly heterogeneous samples and signal defects of complex nature.