**5. Conclusions**

In this paper, we presented a neural network model called IDFNP for FNP image classification, which uses a deep neural network and can achieve accuracy which is comparable to that of neurologists. Key to the performance of the model is an FNP annotated dataset and a deep convolutional network which can classify facial nerve paralysis and facial nerve paresis effectively and accurately. IDFNP combines Inception-v3, which achieves a great result in image classification, and DeepID, which is highly efficient in facial recognition.

The contributions of our method can be summarized as follows: Firstly, a symmetry-based annotation scheme for FNP images with seven different classes is presented. Secondly, using deep neural network on FNP images and cropping the face from the FNP images can eliminate facial deformation for FNP patients and minimize the influence of environmental factors. Thirdly, transfer learning avoids overfitting effectively for a limited range of FNP images. Combining an image classification CNN, such as Inception-v3, and a face recognition CNN like DeepID improves accuracy for the FNP dataset and achieves the same diagnostic accuracy as a neurologist. Fourthly, our method is validated against the performance of other well-known methods, which serves as proof that IDFNP is suitable for FNP classification and can effectively assist neurologists in clinical diagnosis.

In terms of clinical diagnosis, future work will be needed to apply IDFNP performance to other facial diseases or diseases which can be identified visually. On the one hand, more detailed diagnosis of facial paralysis would further aid neurologists in their work. In the future, we plan to undertake a more in-depth study of the position and the degree of disease. On the other hand, we can extend our findings to other conditions. For example, one of the symptoms of a stroke is facial asymmetry, which is very similar to the symptoms of FNP. If IDFNP can diagnose strokes and distinguish various degrees of facial stroke images and facial nerve paralysis images, then preventive treatment for strokes based on facial images can be realized. Given that modern smartphones and PCs are power tools of

deep learning, with the help of the IDFNP results, citizens will have an enhanced ability to obtain an automated assessment for these diseases that may prompt them to visit a specialized physician.

The evaluation results produced by our methods are mostly consistent with the subjective assessment of doctors. Our methods can help clinicians to decide on a specific therapy for each patient, and for the most affected region of the face as reference.

Given that more and more FNP patients are being treated, high-accuracy diagnosis from FNP images can save expert clinicians and neurologists considerable time and decrease the frequency of misdiagnosis. Furthermore, we hope that this technology will enable greater widespread use of FNP images through photography as a diagnostic tool in places where access to a neurologist is limited.

**Author Contributions:** Conceptualization, A.S. and Z.W.; Data curation, A.S., Z.W., and X.D. (Xuehai Ding); Formal analysis, X.D. (Xuehai Ding); Funding acquisition, A.S.; Investigation, X.D. (Xuehai Ding); Methodology, A.S.; Project administration, A.S.; Resources, A.S. and X.D.; Software, Z.W. and Q.H.; Supervision, X.D. (Xuehai Ding); Validation, Z.W., Q.H., and X.D. (Xinyi Di); Visualization, Q.H.; Writing—original draft, Z.W.; Writing—review & editing, Z.W.

**Funding:** This research was funded by the National Natural Science Foundation of China (Grant No. 91630206).

**Acknowledgments:** This study is financially supported by the National Natural Science Foundation of China (Grant No. 91630206). Their support is greatly appreciated.

**Conflicts of Interest:** All authors declared that they have no conflict of interest.
