RamanNet: a lightweight convolutional neural network for bacterial identification based on Raman spectra
Bo Zhou, Yu-Kai Tong, Ru Zhang, Anpei Ye
Abstract
(MRSA and MSSA) on the Bacteria-ID dataset, respectively. Moreover, it achieved an average accuracy of 96.04% on the PKU-bacterial dataset. The RamanNet model benefited from fewer model parameters that can be quickly trained even using CPU. Therefore, our method has the potential to rapidly and accurately identify bacterial species based on their Raman spectra and can be easily extended to other classification tasks based on Raman spectra.
Topics & Concepts
Convolutional neural networkRaman spectroscopyIdentification (biology)Computer scienceChemistryBiological systemMaterials sciencePattern recognition (psychology)Artificial intelligencePhysicsBiologyOpticsBotanySpectroscopy Techniques in Biomedical and Chemical ResearchSpectroscopy and Chemometric AnalysesBiosensors and Analytical Detection