Classification of Vaginal Cleanliness Grades through Surface‐Enhanced Raman Spectral Analysis via The Deep‐Learning Variational Autoencoder–Long Short‐Term Memory Model

Classification of Vaginal Cleanliness Grades through Surface‐Enhanced Raman Spectral Analysis via The Deep‐Learning Variational Autoencoder–Long Short‐Term Memory Model

In this study, it is aimed to establish a novel method based on a deep‐learning‐guided surface‐enhanced Raman spectroscopy (SERS) technique to achieve rapid and accurate classification of vaginal cleanliness levels. We proposed a variational autoencoder (VAE) approach to enhance spectral quality, co...

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Bibliographic Details
Main Authors: Jia‐Wei Tang, Xin‐Ru Wen, Hui‐Min Chen, Jie Chen, Kun‐Hui Hong, Quan Yuan, Muhammad Usman, Liang Wang
Format: Article
Language:English
Published: Wiley 2024-12-01
Series:Advanced Intelligent Systems
Subjects:
autoencoders
long short‐term memories
machine learnings
vaginal cleanliness gradings
vaginal secretions
Online Access:https://doi.org/10.1002/aisy.202400587
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https://doi.org/10.1002/aisy.202400587

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