## 3 Experiments and Results
### Performance of the Sub-Networks on Large-Scale Dataset  
The large-scale retinal OCT dataset [9] consists of a training set of 83,484 images (37,205 CNV, 11,348 DME, 8616 DRUSEN, 26,315 NORMAL) from 4686 patients and a test set of 1000 images (250 CNV, 250 DME, 250 DRUSEN, 250 NORMAL) from 633 patients. The image-based deep learning (IBDL) algorithms in [9] utilize pre-trained Inception-v3, fix the weights of the convolutional layers and finetune the last dense (softmax) layer. In this article, we finetune all the layers of the sub-networks. The training of layers is performed in batches of 32 using Adam [33] optimizer with learning rate of 0.001, \(\beta_{1}\) of 0.9, \(\beta_{2}\) of 0.999, decay rate of 0.3 (Inception-v3 and ResNet50) or 0.01 (DenseNet121). Training is run for 20 epochs, which guarantees convergence. Images are resized to 299 \(\times\) 299 (Inception-v3) or 224 \(\times\) 224 (ResNet50 and DenseNet121). Table 4 compares the performance of the sub-networks and the results in [9].  
The results demonstrate the higher performance of all sub-networks compared against algorithm in [9], which means that method of finetuning all layers is better than finetuning the last layer after feature extraction.  
In the case of Inception-v3, \"mixed6\", \"mixed8\" and \"mixed10\" reach the highest accuracy of 99.70%. Among them, \"mixed6\" is the most effective and has only 32% trainable parameters of \"mixed10\".  
As for ResNet50, the \"Ac40\" model performs best with the accuracy of 99.65%, sensitivity of 99.30%, specificity of 99.77% and has only 36% trainable parameters of \"Ac49\".  
In DenseNet121, both \"C5_b4\" and \"C5_b14\" obtain the accuracy of 99.80%. Taking computational complexity into account, \"C5_b4\" (79% of trainable parameters in \"C5_b14\") is best among DenseNet121 sub-networks.  
Overall, \"C5_b4\" is the best model, which reaches the highest accuracy of 99.80% among all the sub-networks. In the meantime, there are only 5,235,972 trainable parameters in \"C5_b4\", less than 6,819,492 in \"Mixed6\" and 8,562,692 in \"Ac40\". In order to further validate the results, we conduct the experiments of \"C5_b4\" five times and calculate the mean \(\pm\) standard deviation of the value of metrics. In each experiment, we mix the training set and test set and resample 83,484 images (37,205 CNV, 11,348 DME, 8616 DRUSEN, 26,315 NORMAL) as a new training set and 1000 images (250 CNV, 250 DME, 250 DRUSEN, 250 NORMAL) as new test set. After multiple experiments, \"C5_b4\" obtains accuracy of 99.76 \(\pm\) 0.04%, sensitivity of 99.52 \(\pm\) 0.08% and specificity of 99.84 \(\pm\) 0.03%. Results  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline  & & **Accuracy** & **Sensitivity** & **Specificity** \\ \hline IBDL [9] & & 96.60 & 97.80 & 97.40 \\ \hline \multirow{6}{*}{Sub-networks of Inception-v3} & Mixed6 & 99.70 & 99.40 & 99.80 \\  & Mixed7 & 99.35 & 98.70 & 99.57 \\  & Mixed8 & 99.70 & 99.40 & 99.80 \\  & Mixed9 & 99.55 & 99.10 & 99.70 \\  & Mixed10 & 99.70 & 99.40 & 99.80 \\ \hline \multirow{6}{*}{Sub-networks of ResNet50} & Ac37 & 99.60 & 99.20 & 99.73 \\  & Ac40 & 99.65 & 99.30 & 99.77 \\  & Ac43 & 99.45 & 98.90 & 99.63 \\  & Ac46 & 99.60 & 99.20 & 99.7  
demonstrate the improvement in diagnostic performance and the decline in trainable parameters of \"C5_b4\" compared to \"mixed6\" and \"ac40\" as expected.