# 2D Winograd CNN Chip for COVID-19 and Pneumonia Detection
## 4 Discussion
### Model Analysis  
In terms of models, we found that the Sigmoid function would not be able to train without Batch Normalization when training these images. When this problem occurs, it can be observed that the training loss will not decrease and the prediction accuracy will not increase after multiple pieces of training, and the valid loss is the same, so we follow the records of the literature after the convolutional layer of each model. Both have added batch normalization to prevent gradient vanishing issues [46].  
Moreover, it can be observed that although we have added the layer of LSTM or GRU basic on the CNN model, the parameters in the result are similar because we have the layer of convolutional in the trimming model alone. Therefore, according to Table 2, it can be seen that the accuracy rate becomes lower, especially after adding the layer of LSTM, and the accuracy rate becomes lower than other models.  
It can also be seen that in this experiment, the general CNN model performs the best, but the effect of the long and short-term memory layer in the CNN model is not very good. Presumably, in the training of this grayscale image, a convolutional model was used. The effect will be better than long short-term memory. For VGG16 in this experiment, as mentioned earlier, we also added batch normalization to each layer. What is more interesting is that in the first 20 epochs, we can see that the loss of VGG16 will suddenly increase, but the model does not explode for this reason. Although the basic CNN also has this problem, compared with VGG16, this problem becomes less obvious, and the LSTM and GRU models do not have this problem.  
In addition, in Figure 7, it can also be observed that loss and accuracy are related. If there is an epoch where the loss suddenly changes significantly, you can see that the effect of training at that time will lead to a significant decrease in accuracy. The reason for this is that we have issues that require special attention. At present, according to our speculation, it is because there are CNN and VGG16 in the epochs that are not trained according to the time record of LSTM or GRU, so this problem occurs because the training gradient of a certain Layer in training disappears, resulting in the result showing a difference.  
Besides, we compared our proposed model on COVID-19 diagnosis tasks with those of previous SOTA COVID-19 screening methods, which included [PERSON] [47], [PERSON] [48], and [PERSON] [47] in Table 3. [PERSON] [47] presented an infection region-specific segmentation technique based on a random forest model to distinguish COVID-19 from other forms of pneumonia using CT exams [47]. This study reported 83.30% accuracy [47]. [PERSON] [48] designed a COVID-Net framework tailored to identify COVID-19 from chest radiography [48] and  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F-Score** \\ \hline Proposed & 87.87\% & 88.48\% & 86.67\% & 87.37\% \\ Bi-LSTM & 81.83\% & 80.52\% & 79.08\% & 79.57\% \\ GRU & 82.84\% & 80.99\% & 82.39\% & 81.55\% \\ VGG16 & 85.49\% & 84.56\% & 84.21\% & 84.17\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results indicators.  
achieved 82.9% accuracy. [PERSON] [47] proposed an AI-based technique to screen coronavirus from healthy and viral pneumonia using CT exams and reported 86.7% accuracy [47]. Comparing these forward-looking methods, our proposed method has higher accuracy.