## 5 Discussion
### Comparison with Deep-Learning-Based IQA Methods  
In this part of the experiment, we compared the proposed model with two deep-learning-based methods: TwostreamIQA [15] and WaDIQaM-FR [31]. TwostreamIQA uses the gradient image as the features to be learned. WaDIQaM-FR adds a patch weight estimate module at the end of the feature extraction layers, and the predicted score of each image is the weighted sum of all patch scores. WaDIQaM-FR is a kind of FR-IQA method that needs a reference image. We used the assumption in [32]--that is, the clearer the image is, the greater the difference between its Gaussian blurred image and the original image is. A Gaussian blur operation with a kernel size of 21 and sigma value of 3.5 was performed on all datasets, and then the original images and corresponding Gaussian blurred images were used as reference images and distorted images, respectively. We repeated the training process five times and adopted a similar optimal model selection method. The comparison of average \"acc\" on the test set, leucorrhea dataset, and blood dataset is shown in Figure 11. We also tested the WaDIQaM-NR [31] method, but its prediction accuracy on the validation set was lower than 97%.  
From the results, we can see that the proposed model outperformed the other two deep-learning-based IQA methods. Both the TwostreamIQA and WaDIQaM-FR methods achieved excellent prediction accuracy on the feces dataset, and their average \"acc\" on the test set was 96.539% and 97.885%, respectively. However, they could not achieve valid results on the leucorrhea dataset. Although we normalized the gradient images in advance so that the gradient features of different images were at the same magnitude, the gradient distribution of images in different datasets was still different. Therefore, the deep model trained on the feces dataset by the TwostreamIQA method was only applicable for the feces dataset. The patch score and weight in WaDIQaM-FR are trainable parameters, which were trained on the feces dataset. As a result, the predicted score and weight in the feces dataset were exact but they were inaccurate in the leucorrhea dataset.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & & **Test Set** & \multicolumn{4}{c}{**Leucorrhea Dataset**} & \multicolumn{3}{c}{**Blood Dataset**} \\ \cline{2-10}  & **Model** & \(t_{0}\) & \(t_{1}\) & **Acc** & **Srocc** & \(t_{0}\) & \(t_{1}\) & **Acc** & \(t_{0}\) & \(t_{1}\) & **Acc** \\ \hline \multirow{2}{*}{Round 1} & L0 & 137 & 64 & 96.635\% & 0.8958 & 289 & 341 & 90.129\% & 87 & 41 & 98.462\% \\  & L1 & 135 & 69 & 98.077\% & 0.9075 & 314 & 332 & 92.418\% & 77 & 53 & 100.00\% \\ \hline \multirow{2}{*}{Round 2} & L0 & 129 & 71 & 96.154\% & 0.8811 & 305 & 320 & 89.413\% & 97 & 32 & 99.231\% \\  & L1 & 133 & 67 & 96.154\% & 0.8974 & 346 & 308 & 93.562\% & 76 & 54 & 100.00\% \\ \hline \multirow{2}{*}{Round 3} & L0 & 119 & 80 & 95.673\% & 0.8673 & 310 & 270 & 82.976\% & 85 & 45 & 100.00\% \\  & L1 & 130 & 74 & 98.077\% & 0.9182 & 362 & 247 & 87.124\% & 88 & 39 & 97.692\% \\ \hline \multirow{2}{*}{Round 4} & L0 & 134 & 65 & 95.673\% & 0.9012 & 116 & 270 & 55.222\% & 53 & 74 & 97.692\% \\  & L1 & 137 & 66 & 97.596\% & 0.8943 & 254 & 371 & 89.413\% & 85 & 45 & 100.00\% \\ \hline \multirow{2}{*}{Round 5} & L0 & 138 & 63 & 96.635\% & 0.8844 & 303 & 345 & 92.704\% & 99 & 29 & 98.462\% \\  & L1 & 130 & 69 & 95.673\% & 0.9049 & 288 & 354 & 91.845\% & 84 & 63 & 97.692\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: The performance of model trained without auxiliary outputs.  
In order to further prove the effectiveness of GMANet, in the Supplementary Materials, we demonstrate the performance of 37 types of traditional IQA methods on finding the clearest human fecal microscopic image in the autofocus process. Furthermore, we analyze the reasons for the poor performance of traditional IQA methods.