Feilong Yue1, Cheng Chen2, Ziwei Yan3, Chen Chen3, Zhiqi Guo1, Zhaoxia Zhang4, Zhaoyun Chen4, Fengbo Zhang4, Xiaoyi Lv5. 1. College of Software, Xinjiang University, Urumqi 830046, China; Key Laboratory of Software Engineering Technology, Xinjiang University, Urumqi 830046, China. 2. College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China. Electronic address: chenchengoptics@gmail.com. 3. College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China. 4. Xinjiang Medical University Affiliated First Hospital, Urumqi 830000, China. 5. College of Software, Xinjiang University, Urumqi 830046, China; Key Laboratory of Software Engineering Technology, Xinjiang University, Urumqi 830046, China. Electronic address: xjuwawj01@163.com.
Abstract
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.
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.