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Fourier transform infrared spectroscopy combined with deep learning and data enhancement for quick diagnosis of abnormal thyroid function

机译：傅里叶变换红外光谱结合深入学习和数据增强，以便快速诊断异常甲状腺功能

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摘要

Background: To evaluate the Fourier transform infrared spectroscopy (FT-IR) combined with deep learning models to allow for quick diagnosis of abnormal thyroid function.Materials and methods: Serum samples of 199 patients with abnormal thyroid function and 183 healthy patients were collected by infrared spectroscopy data and combined with different decibel noise for data expansion. The data were directly imported into three deep models: multilayer perceptron (MLP), a long-short-term memory network (LSTM), and a convolutional neural network (CNN), and 10-fold cross-validation was used to evaluate the performance of the model.Results: The accuracy rates of the three models using the original data were 91.3 %, 88.6 % and 89.3 %, and the accuracy rates of the three models after data enhancement were 92.7 %, 93.6 % and 95.1 %.Conclusion: The results of this study indicated that the use of large sample serum infrared spectroscopy data combined with deep learning algorithms is a promising method for the diagnosis of abnormal thyroid function.
机译：背景：为了评估傅里叶变换红外光谱（FT-IR）与深度学习模型相结合，以便快速诊断异常甲状腺功能。通过红外线收集1999例异常甲状腺功能患者的血清样本和183例健康患者的血清样本光谱数据和结合不同的分贝噪声进行数据扩展。将数据直接导入三个深度模型：多层erceptron（MLP），长期内存网络（LSTM）和卷积神经网络（CNN）和10倍交叉验证用于评估性能在模型。结果：使用原始数据的三种模型的精度率为91.3％，88.6％和89.3％，数据增强后的三种型号的准确性率为92.7％，93.6％和95.1％。结论：本研究的结果表明，使用大型样品血清红外光谱数据与深学习算法相结合，是诊断异常甲状腺功能的有希望的方法。

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  • 来源
    《Photodiagnosis and Photodynamic Therapy》 |2020年第12期|101923.1-101923.5|共5页
  • 作者

    Yue Feilong; Chen Cheng; Yan Ziwei; Chen Chen; Guo Zhiqi; Zhang Zhaoxia; Chen Zhaoyun; Zhang Fengbo; Lv Xiaoyi;

  • 作者单位

    Xinjiang Univ Coll Software Urumqi 830046 Peoples R China|Xinjiang Univ Key Lab Software Engn Technol Urumqi 830046 Peoples R China;

    Xinjiang Univ Coll Informat Sci & Engn Urumqi 830046 Peoples R China;

    Xinjiang Univ Coll Informat Sci & Engn Urumqi 830046 Peoples R China;

    Xinjiang Univ Coll Informat Sci & Engn Urumqi 830046 Peoples R China;

    Xinjiang Univ Coll Software Urumqi 830046 Peoples R China|Xinjiang Univ Key Lab Software Engn Technol Urumqi 830046 Peoples R China;

    Xinjiang Med Univ Affiliated Hosp 1 Urumqi 830000 Peoples R China;

    Xinjiang Med Univ Affiliated Hosp 1 Urumqi 830000 Peoples R China;

    Xinjiang Med Univ Affiliated Hosp 1 Urumqi 830000 Peoples R China;

    Xinjiang Univ Coll Software Urumqi 830046 Peoples R China|Xinjiang Univ Key Lab Software Engn Technol Urumqi 830046 Peoples R China;

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  • 正文语种 eng
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  • 关键词

    FT-IR; Abnormal thyroid function; Serum; Deep learning; Data enhancement;

    机译：FT-IR;异常甲状腺功能;血清;深入学习;数据增强;

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