Deep Learning for NMR Spectra: CNN Classification Using Autoencoder-Generated Latent Features

Nuclear Magnetic Resonance (NMR) spectroscopy is crucial for molecular structure analysis but poses challenges due to high dimensionality and noise. We propose a deep learning approach that combines autoencoders and Convolutional Neural Networks (CNNs) for improved NMR spectra classification. An auto encoder reduces the data to compact latent representations, which a CNN then classifies. Our method, evaluated on a comprehensive NMR dataset, shows significant accuracy improvements over direct CNN classification on raw spectra and other baselines. The autoencoder effectively captures essential features, enhancing the CNN's performance. This framework addresses NMR classification challenges, offering a robust tool for researchers and practitioners. NMR Classification which mainly is dealt manually for now and classified by the spectra peaks as case or control, can be now classified using our proposed framework. In this we are achieving 80.9% accuracy on NMR Spectra dataset. This framework is an improved solution for NMR spectroscopy with least human intervention.