*3.4. Comparison with Other Deep Convolution Neural Networks Models*

Because our dataset's scale is not large enough to train models directly, for every model compared, we removed the final classification layer from the network, retrained it with our dataset, and leveraged the natural image features learned by the ImageNet pretrained network, a technique known as transfer learning. We chose Inception-v3, Inception-v4, Inception-ResNet-v1, Inception-ResNet-v2, DeepID, and ResNet, which in recent years have shown the best results in image classification. The results are shown in Table 9.



For accuracy, IDFNP CNN outperforms all the other CNNs for the FNP dataset. All the other CNNs were designed for the ImageNet Challenge Database, which has 1000 object classes and are optimized for image classification, which is quite relevant for the present application. Our original plan for diagnosing FNP was to use transfer learning with Inception-ResNet-v2 directly. However, the result did not match the accuracy of neurologists. Considering that FNP classification is a face classification, combining DeepID CNN with Inception-v3 CNN improves accuracy.

#### **4. Discussion**

As we see from Table 7, neurological agreement exceeds our method in MV2, MV5, and MV6. However, neurologists take too long examining FNP images, as each such examination takes at least

10 s. Our method takes a few milliseconds per FNP image and is thus more efficient, while its accuracy is comparable to that of neurologists. Our previous method takes much longer per FNP image by calculating facial asymmetry with traditional computational methods, while only its accuracy in MV0 on RgAs is higher. Furthermore, our previous method requires more standard images like face angle, image clarity, and lighting conditions.

As we see from Table 8, the accuracy of FNP classification when using Sajid's method was 92.6%. The accuracy of FNP in Neely's method [28] is 95%, which is lower than our method. HC [28] used RBF with 0/1 disagreement to measure accuracy of FNP movements. Even with 1 disagreement, which allows for more experimental errors, the result is significantly worse than ours. Wang [29,30] used SVM with RBF to measure accuracy. The result showed our method is better than their method in MV2–6. In MV1, their accuracy is not much higher than ours. Although they didn't calculate the accuracy of MV0, we can still see from the rest of the results that our method yields superior results.

As we see from Table 9, these models have strong generalization ability for different datasets, but because their design was optimized for their main, that is, image classification, the final training results of these models are not as good as our model. We also see that Inception-v3, upon which our own design was based, achieved only 93.3% accuracy. Therefore, there is still considerable potential for the optimization of this excellent image classification model for specific applications, especially with residual network derivatives like Inception-ResNet-v2.

Meanwhile, on the basis of our findings, clinicians can quickly obtain the degree of facial paralysis according to different facial movements. Clinicians can make a prediagnosis of facial nerve paralysis based on patients' facial movements, which will be used as a reference for their final diagnosis. For example, the result of one patient in MV1 (Eye closed), MV2 (Eyebrows raised), and MV4 (Grinning) was L3, and the result of the patient in other movements was N or L1, which corresponds to a prediagnosis that severe paralysis is present in the in left orbicularis oculi muscle.
