## 3 Experiments and Results
### Result Comparisons
In this experiment, we focus on comparing the proposed method with fine-tuning a pre-trained network, including performance, training time, etc. Fine-tuning a pre-trained network is adopted by Ref. [31]. We conduct an experiment on the Beijing clinic dataset. Half of the clinic dataset are chosen as training sets and the rest as testing images. The experiment is repeated 10 times with 50 epochs for each time. We use Inception V3 as the pre-trained network (pre-trained on ImageNet dataset) instead of GoogLeNet in Ref. [31] for fairness. In this experiment, we fine-tune different layers for comparison: (1) The weights of fully connected layers are randomly initialized and the other weights of the network are fixed (model 1). (2) The weights of the InceptionE2 layer and the fully connected layer can be updated while the others are fixed (model 2). (3) The weights of the InceptionE1, InceptionE2 and the fully connected layer can be updated while the others are fixed (model 3). (4) All of the weights in the network can be updated (model 4). The mean sensitivity of each epoch is shown in Figure 4. After 50 epochs for training, the sensitivity of each model (from model 1 to model 4) are 63.81%, 70.36%, 82.86%, and 98.73%, respectively, and ours is 98.61%. In terms of training efficiency, the average time of the fine-tuning method to train 50 epochs is 474 s while the proposed method is 95 s. To prove the computational efficiency advantage of our method, we try to use only the CPU to perform calculation, \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Partition** & **Methods** & **Overall-Acc** & **Overall-Se** & **Overall-Sp** \\ \hline \multirow{3}{*}{\(1/4\) dataset} & ScSPM & 97.46 & 96.24 & 98.08 \\  & IBDL & 92.54 & 89.18 & 94.27 \\  & Ours & 98.22 & 97.35 & 98.66 \\ \hline \multirow{3}{*}{\(1/2\) dataset} & ScSPM & 97.75 & 96.67 & 98.30 \\  & IBDL & 94.57 & 92.03 & 95.85 \\ \cline{1-1}  & Ours & 98.86 & 98.30 & 99.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Overall classification results (%) of different methods on clinic dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Classes** & **Accuracy** & **Sensitivity** & **Specificity** \\ \hline \multirow{3}{*}{ScSPM} & AMD & 97.35 \(\pm\) 0.58 & 96.19 \(\pm\) 0.85 & 97.94 \(\pm\) 0.45 \\  & DME & 97.17 \(\pm\) 0.44 & 93.81 \(\pm\) 0.51 & 98.87 \(\pm\) 0.46 \\  & NOR & 97.87 \(\pm\) 0.15 & 98.73 \(\pm\) 0.49 & 97.44 \(\pm\) 0.08 \\ \hline \multirow{3}{*}{IBDL} & AMD & 91.23 \(\pm\) 0.38 & 85.40 \(\pm\) 0.81 & 94.25 \(\pm\) 0.58 \\  & DME & 94.77 \(\pm\) 0.29 & 96.83 \(\pm\) 0.22 & 93.65 \(\pm\) 0.57 \\  & NOR & 91.63 \(\pm\) 0.40 & 85.32 \(\pm\) 0.40 & 94.92 \(\pm\) 0.66 \\ \hline \multirow{3}{*}{Ours} & AMD & 98.51 \(\pm\) 0.19 & 98.14 \(\pm\) 0.49 & 98.69 \(\pm\) 0.44 \\  & DME & 97.80 \(\pm\) 0.35 & 94.57 \(\pm\) 0.76 & 99.43 \(\pm\) 0.31 \\ \cline{1-1}  & NOR & 98.35 \(\pm\) 0.20 & 99.33 \(\pm\) 0.41 & 97.85 \(\pm\) 0.38 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Classification results (%) on \(1/4\) clinic dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Classes** & **Accuracy** & **Sensitivity** & **Specificity** \\ \hline \multirow{3}{*}{ScSPM} & AMD & 97.75 \(\pm\) 0.21 & 96.43 \(\pm\) 0.58 & 98.43 \(\pm\) 0.08 \\  & DME & 97.60 \(\pm\) 0.29 & 95.48 \(\pm\) 0.89 & 98.67 \(\pm\) 0.09 \\  & NOR & 97.91 \(\pm\) 0.31 & 98.10 \(\pm\) 0.84 & 97.81 \(\pm\) 0.40 \\ \hline \multirow{3}{*}{IBDL} & AMD & 93.36 \(\pm\) 0.32 & 88.84 \(\pm\) 2.78 & 95.66 \(\pm\) 1.02 \\  & DME & 96.96 \(\pm\) 0.14 & 98.13 \(\pm\) 0.46 & 96.33 \(\pm\) 0.24 \\  & NOR & 93.39 \(\pm\) 0.25 & 89.11 \(\pm\) 2.21 & 95.57 \(\pm\) 0.99 \\ \hline \multirow{3}{*}{Ours} & AMD & 99.01 \(\pm\) 0.30 & 99.02 \(\pm\) 0.39 & 99.01 \(\pm\) 0.37 \\  & DME & 98.51 \(\pm\) 0.27 & 96.34 \(\pm\) 1.08 & 99.60 \(\pm\) 0.20 \\ \cline{1-1}  & NOR & 99.07 \(\pm\) 0.21 & 99.55 \(\pm\) 0.46 & 98.83 \(\pm\) 0.32 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Classification results (%) on \(1/2\) clinic dataset.