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

• Published in: Photodiagnosis and photodynamic therapy 2020-12, Vol.32, p.101923-101923, Article 101923
• Main Authors: Yue, Feilong, Chen, Cheng, Yan, Ziwei, Chen, Chen, Guo, Zhiqi, Zhang, Zhaoxia, Chen, Zhaoyun, Zhang, Fengbo, Lv, Xiaoyi
• Format: Article
• Language: English
• Subjects: Abnormal thyroid function; Data enhancement; Deep Learning; FT-IR; Humans; Photochemotherapy - methods; Photosensitizing Agents; Serum; Spectroscopy, Fourier Transform Infrared; Thyroid Gland
• ISSN: 1572-1000; 1873-1597
• DOI: 10.1016/j.pdpdt.2020.101923

•Infrared spectroscopy can show changes in the molecular level of patients.•Human serum Fourier transform infrared spectroscopy provides initial data.•Data was processed on deep learning models. To evaluate the Fourier transform infrared spectroscopy (FT-IR) combined with deep learning models to allow for quick diagnosis of abnormal thyroid function. 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. 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 %. 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.