Volume 15 Issue 3
May  2022
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Chinese Optics  > 2022  >  15(3): 443-453.
ZHENG Jiang-peng, YU Ping, ZHAO Meng, SHI Fan, SUN Xu-guo, CHEN Sheng-yong. Detection of myocardial amyloidosis by a small number of terahertz spectra with low signal-to-noise ratio[J]. Chinese Optics, 2022, 15(3): 443-453. doi: 10.37188/CO.2021-0223
Citation: ZHENG Jiang-peng, YU Ping, ZHAO Meng, SHI Fan, SUN Xu-guo, CHEN Sheng-yong. Detection of myocardial amyloidosis by a small number of terahertz spectra with low signal-to-noise ratio[J]. Chinese Optics, 2022, 15(3): 443-453. doi: 10.37188/CO.2021-0223
shu

Detection of myocardial amyloidosis by a small number of terahertz spectra with low signal-to-noise ratio

  • 1.

    Learning Intelligent System Engineering Research Center of the Ministry of Education, School of Computer Science and Engineering, Tianjin University of Technology, Tianjin 300384

  • 2.

    School of Medical Laboratory, Tianjin Medical University, Tianjin 300203

Funds:  Supported by National Natural Science Foundation of China (No. 61906133, No. 62020106004, No. 92048301, No. 61703304, No. 61906134, No. 61903275, No. 61902078)
More Information
  • Corresponding author: zh_m@tju.edu.cn; shifan@email.tjut.edu.cn
  • Received Date: 16 Dec 2021
  • Rev Recd Date: 12 Jan 2022
  • Accepted Date: 28 Jan 2022
    • Available Online: 13 Apr 2022
  • Publish Date: 20 May 2022
    Abstract
  • Due to the difficulty of extracting the distinguishable features of a small number of terahertz spectra with low signal-to-noise ratio; second and the over fitting problem of the deep learning model itself caused by too few samples, the application of terahertz spectra and deep learning in myocardial amyloidosis detection exists some challenges. In this paper, we propose a classification model based on multi-modules sequential cascade for real-time detection of myocardial amyloidosis at the algorithm level. Firstly, we collect a small number of low SNR terahertz spectra and preprocess them. Secondly, we construct a deep learning model based on denoising autoencoder, multi-scale feature extraction module and dense connection module. Finally, we use the 5 folds cross validation strategy to predict the lesions to obtain stable and reliable results. The results of 10 times independent repeated experiment and comparative experiment show that this method can classify the spectra with noise accurately and stably, which possesses of a better performance. Experiments under different number of samples show that this method is adaptive to the change of dataset size: an accuracy of 95% is achieved corresponding to 100 samples; when the amount of samples is only 20, the model can still achieve an accuracy of 70%. Therefore, the proposed method is of great significance for the real-time, efficient and safe diagnosis of myocardial amyloidosis.

     

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