Serum Raman spectroscopy combined with Deep Neural Network for analysis and rapid screening of hyperthyroidism and hypothyroidism

Li, Yizhe; Chen, Cheng; Chen, Fang‐Fang; Chen, Chen; Gao, Rui; Yang, Bo; Si, Rumeng; Lv, Xiaoyi

doi:10.1016/j.pdpdt.2021.102382

Cited by 17 publications

(4 citation statements)

References 33 publications

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“…AlexNet is a classic deep learning model. It adds the ReLU activation function behind each convolution layer, which makes the training speed of the model faster 50 . To better adapt to NIR data, this study adjusted AlexNet 31 , 51 .…”

Section: Resultsmentioning

confidence: 99%

A novel fast method for identifying the origin of Maojian using NIR spectroscopy with deep learning algorithms

Chang

et al. 2022

Sci Rep

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Maojian is one of China’s traditional famous teas. There are many Maojian-producing areas in China. Because of different producing areas and production processes, different Maojian have different market prices. Many merchants will mix Maojian in different regions for profit, seriously disrupting the healthy tea market. Due to the similar appearance of Maojian produced in different regions, it is impossible to make a quick and objective distinction. It often requires experienced experts to identify them through multiple steps. Therefore, it is of great significance to develop a rapid and accurate method to identify different regions of Maojian to promote the standardization of the Maojian market and the development of detection technology. In this study, we propose a new method based on Near infra-red (NIR) with deep learning algorithms to distinguish different origins of Maojian. In this experiment, the NIR spectral data of Maojian from different origins are combined with the back propagation neural network (BPNN), improved AlexNet, and improved RepSet models for classification. Among them, improved RepSet has the highest accuracy of 99.30%, which is 8.67% and 0.70% higher than BPNN and improved AlexNet, respectively. The overall results show that it is feasible to use NIR and deep learning methods to quickly and accurately identify Maojian from different origins and prove an effective alternative method to discriminate different origins of Maojian.

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Section: Resultsmentioning

confidence: 99%

A novel fast method for identifying the origin of Maojian using NIR spectroscopy with deep learning algorithms

Chang

et al. 2022

Sci Rep

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“…54,55 Particularly, LSTM networks are the most effective solution to sequence learning 56 and have been recently applied to analyzing Raman spectral data. 25,57,58 LSTM networks are an improved version of RNNs, which are explicitly designed to avoid the long-term dependency problem. LSTM generally comprises four main gates, i.e., the input gate, the forget gate, the output gate and the cell state.…”

Section: Discussionmentioning

confidence: 99%

Development of deep learning algorithms to discriminate giant cell tumors of bone from adjacent normal tissues by confocal Raman spectroscopy

Lau

Law³

et al. 2022

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“…Since water molecules have very low photon cross-sections, they do not affect the components of interest in the liquid [29,33]. Many studies have also used serum samples and Raman spectroscopy, Including screening for hyperthyroidism [34], gastric cancer [35], rats serum is used to screen for Alzheimer's disease [36].…”

Section: Introductionmentioning

confidence: 99%

Convenient determination of serum HER‐2 status in breast cancer patients using Raman spectroscopy

Li,

Wang

et al. 2024

Journal of Biophotonics

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Given the significant therapeutic efficacy of anti‐HER‐2 treatment, the HER‐2 status is a crucial piece of information that must be obtained in breast cancer patients. Currently, as per guidelines, HER‐2 status is typically acquired from breast tissue of patients. However, there is growing interest in obtaining HER‐2 status from serum and other samples due to the convenience and potential for dynamic monitoring. In this study, we have developed a serum Raman spectroscopy technique that allows for the rapid acquisition of HER‐2 status in a convenient manner. The established HER‐2 negative and positive classification model achieved an Area Under the Curve of 0.8334. To further validate the reliability of our method, we replicated the process using immunohistochemistry and in situ hybridization. The results demonstrate that serum Raman spectroscopy, coupled with artificial intelligence algorithms, is an effective technical approach for obtaining HER‐2 status.This article is protected by copyright. All rights reserved.

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Serum Raman spectroscopy combined with Deep Neural Network for analysis and rapid screening of hyperthyroidism and hypothyroidism

Cited by 17 publications

References 33 publications

A novel fast method for identifying the origin of Maojian using NIR spectroscopy with deep learning algorithms

A novel fast method for identifying the origin of Maojian using NIR spectroscopy with deep learning algorithms

Development of deep learning algorithms to discriminate giant cell tumors of bone from adjacent normal tissues by confocal Raman spectroscopy

Convenient determination of serum HER‐2 status in breast cancer patients using Raman spectroscopy

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