The Proceedings of The Computational Mechanics Conference
Online ISSN : 2424-2799
2024.37
Session ID : OS-2210
Conference information

Proposal of Data Augmentation Method for Metabolome Estimation from Raman Spectral
*Masato MASUDAHiroki NISHIDaisuke YAMANAKAYasushi NAKABAYASHIRyuji SHIOYAFumihiko HAKUNOTakafumi MIYAMOTOShin-Ichiro TAKAHASHIYoshiaki TAMURA
Author information
CONFERENCE PROCEEDINGS RESTRICTED ACCESS

Details
Abstract

Generally, in deep learning of image recognition, there is an established data augmentation method to increase the training data. However, there is no established data augmentation method that can handle numerical data analyzed in experiments, etc. Therefore, we propose a method for data augmentation of such numerical data. We are developing a method to analyze serum components using a Raman spectrometer and to estimate the metabolome and other components in serum from Raman spectral data. Serum samples are samples obtained through animal experiments, and the number of samples is limited. Although the learning accuracy of deep learning decreased when the number of samples was small, the proposed data augmentation method improved the learning accuracy.

Content from these authors
© 2024 The Japan Society of Mechanical Engineers
Previous article Next article
feedback
Top