## 4 Experimental Results and Dataset
### Experimental Results  
In order to eliminate the performance bias of the proposed method, we repeated the whole training process five times, and the corresponding results on the test set, leucorrhea dataset, and blood dataset are shown in Table 1. \"srocc\" represents the average SROCC value in the evaluation image group. The selection of optimal models on the validation set is described in Section 3.4. To prevent under-fitting or over-fitting, the L\({}_{0}\) model should also meet the following conditions: \"acc\" should be higher than 98% and lower than 99%; \(t_{0}\) should be between 120 and 129. The L\({}_{1}\) model only needs to meet the requirement that \"acc\" is higher than 98%. Blood and leucorrhea microscopic images were rescaled to the size of 1024 \(\times\) 1536 and 768 \(\times\) 1152. The same normalization method was adopted and the patch cropping step was 256 and 384 in the horizontal and vertical direction for them. The size of \(\varphi\) was set to 32 \(\times\) 32 for the blood microscopic image and 8 \(\times\) 8 for the leucorrhea microscopic image. We used the tensorflow2 framework to build our algorithm and ran it on a RTX 3090 GPU. It can be seen from Table 1 that: L\({}_{0}\) can easily over-fit on the validation set, and its prediction accuracy on the test set is unstable; the L\({}_{0}\) in Round 5 over-fits on the feces dataset and cannot be used for the leucorrhea dataset; L\({}_{1}\) achieves good prediction accuracy on the feces dataset and it is more consistent with HVS (\(t_{0}\) on test set is higher); L\({}_{1}\) can improve the prediction accuracy on the leucorrhea dataset better than L\({}_{0}\), especially the L\({}_{0}\) in Round 5; for the blood dataset with simple compositions, both L\({}_{0}\) and L\({}_{1}\) can achieve excellent results. In the training process, we also tried to use the model with the largest average SROCC value as the optimal model, but the model prediction accuracy was unstable. As shown in Figure 7, for one fecal microscopic image group in the test set, the blue and green curve represent the predicted score curve calculated by model L\({}_{1}\) (Round 3) and the annotated score curve, respectively. Although the image capture order \(i^{p}\) is equal to \(i^{m}\) in this image group, the predictions of blurred image scores are inaccurate, leading to a low SROCC value, which is 0.5016. Considering that our goal was to find the clearest image, we only regarded the SROCC value as a reference, not as an evaluation metric for model selection. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Test Set**} & \multicolumn{4}{c}{**Leucorrhea Dataset**} & \multicolumn{4}{c}{**Blood Dataset**} \\ \cline{2-11}  & **Model** & \(\mathbf{t}_{0}\) & \(\mathbf{t}_{1}\) & **Acc** & **Srocc** & \(\mathbf{t}_{0}\) & \(\mathbf{t}_{1}\) & **Acc** & \(\mathbf{t}_{0}\) & \(\mathbf{t}_{1}\) & **Acc** \\ \hline \multirow{2}{*}{Round 1} & L\({}_{0}\) & 128 & 71 & 95.673\% & 0.8984 & 325 & 312 & 91.130\% & 97 & 32 & 99.231\% \\  & L\({}_{1}\) & 130 & 74 & 96.154\% & 0.8925 & 399 & 278 & 96.853\% & 90 & 39 & 99.231\% \\ \hline \multirow{2}{*}{Round 2} & L\({}_{0}\) & 124 & 73 & 94.712\% & 0.8774 & 404 & 233 & 91.130\% & 77 & 52 & 99.231\% \\  & L\({}_{1}\) & 134 & 66 & 96.154\% & 0.8942 & 399 & 255 & 93.562\% & 83 & 47 & 100\% \\ \hline \multirow{2}{*}{Round 3} & L\({}_{0}\) & 133 & 66 & 95.673\% & 0.9038 & 362 & 300 & 94.707\% & 93 & 36 & 99.231\% \\  & L\({}_{1}\) & 134 & 71 & 98.558\% & 0.8884 & 389 & 276 & 95.136\% & 85 & 42 & 97.692\% \\ \hline \multirow{2}{*}{Round 4} & L\({}_{0}\) & 139 & 58 & 94.712\% & 0.9039 & 307 & 348 & 93.705\% & 82 & 46 & 98.462\% \\  & L\({}_{1}\) & 140 & 63 & 97.596\% & 0.9030 & 314 & 346 & 94.421\% & 107 & 23 & 100\% \\ \hline \multirow{2}{*}{Round 5} & L\({}_{0}\) & 126 & 75 & 96.635\% & 0.8958 & 306 & 271 & 82.546\% & 80 & 49 & 99.231\% \\  & L\({}_{1}\) & 136 & 67 & 97.596\% & 0.8997 & 387 & 258 & 92.275\% & 64 & 65 & 99.231\% \\ \hline \hline \end{tabular}
\end{table}