Screening and Diagnosis of Chronic Pharyngitis Based on Deep Learning
Abstract
:1. Introduction
2. Materials and Methods
2.1. Deep Learning Methods
2.2. Convolutional Neural Network Theory
2.3. Back Propagation Algorithm
2.4. Topological Structure of Convolutional Neural Networks
2.5. Spectrogram
2.6. Different Deep Learning
2.6.1. Recurrent Neural Networks
2.6.2. Deep Belief Networks
2.6.3. Convolutional Neural Network (SqueezeNet)
3. Results
3.1. Data Preprocessing
3.2. Experimentation
3.3. Learning Rate Comparison Experiment
3.4. Output Node Experiment
3.5. Momentum Experiment
3.6. Comparison of Different Deep Learning Effects
4. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Learning Rate/Number of Iterations | 50 | 100 | 150 | 200 | 250 |
---|---|---|---|---|---|
0.0001 | 63.72% | 65.96% | 72.19% | 72.41% | 72.86% |
0.001 | 68.18% | 69.44% | 69.79% | 69.79% | 69.79% |
0.01 | 72.59% | 82.57% | 82.73% | 82.91% | 82.94% |
0.1 | 37.21% | 45.63% | 48.39% | 51.06% | 51.06% |
0.5 | 21.54% | 22.82% | 23.77% | 23.77% | 23.76% |
Momentum Value | Recognition Rate |
---|---|
0.55 | 0.59 |
0.75 | 0.67 |
0.95 | 0.89 |
1.35 | 0.73 |
Algorithm | Recognition Rate |
---|---|
DBN | 69.3% |
RNN | 74.7% |
CNN (AlexNet) | 81.2% |
CNN (SqueezeNet) | 80.7% |
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Li, Z.; Huang, J.; Hu, Z. Screening and Diagnosis of Chronic Pharyngitis Based on Deep Learning. Int. J. Environ. Res. Public Health 2019, 16, 1688. https://doi.org/10.3390/ijerph16101688
Li Z, Huang J, Hu Z. Screening and Diagnosis of Chronic Pharyngitis Based on Deep Learning. International Journal of Environmental Research and Public Health. 2019; 16(10):1688. https://doi.org/10.3390/ijerph16101688
Chicago/Turabian StyleLi, Zhichao, Jilin Huang, and Zhiping Hu. 2019. "Screening and Diagnosis of Chronic Pharyngitis Based on Deep Learning" International Journal of Environmental Research and Public Health 16, no. 10: 1688. https://doi.org/10.3390/ijerph16101688