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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主要な著者:	Jia‐Wei Tang, Xin‐Ru Wen, Hui‐Min Chen, Jie Chen, Kun‐Hui Hong, Quan Yuan, Muhammad Usman, Liang Wang
フォーマット:	Artigo
言語:	Inglês
出版事項:	Wiley 2024-12-01
シリーズ:	Advanced Intelligent Systems
主題:	autoencoders long short‐term memories machine learnings vaginal cleanliness gradings vaginal secretions
オンライン･アクセス:	https://doi.org/10.1002/aisy.202400587
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