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

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.