# Classification of OCT Images of the Human Eye Using Mobile Devices
## 4 Experimental Selection of Neural Networks
### Network Tests After Conversion to the TensorFlow Lite Model  
All networks were converted to the TensorFlow Lite format and tested on the same set of images. The resulting Accuracy, Precision, Recall, and F1-Score are presented in Table 9. Figure 6 illustrates the obtained confusion matrix for all network architectures. Comparing the results for the network with three convolutional layers before and after conversion, the accuracy dropped by 10%, and the F1-score dropped by as much as 13%. Analyzing the confusion matrix in Figure 6a, it can be observed that the VMT class has the greatest number of correct predictions. However, classes CNV and DRUSEN (which are assigned to the DME category) have a significant number of incorrect predictions. The DRUSEN class remained with the fewest correct predictions. For the VGG16 network, a 13% decrease in both accuracy and F1-score can be observed. After conversion to the TensorFlow Lite format, it can be seen in Figure 6b that the NORMAL class obtained the largest number of correct predictions, while before the conversion, it was the VMT class. The number of correct predictions for the DME class increased compared to the results before conversion. The VMT and DRUSEN classes resulted in the smallest number of positive predictions. When comparing the results for both formats, the accuracy and F1-score of the InceptionV3 network dropped by 7% and 8%, respectively. The confusion matrix in Figure 6c shows that the network in the TensorFlow Lite format obtained the largest number of correct predictions for the VMT class and performed significantly worse with the NORMAL and DRUSEN classes. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline
3-layer CNN & 0.64 & 0.64 & 0.64 & 0.64 \\ VGG16 & 0.83 & 0.83 & 0.82 & 0.83 \\ InceptionV3 & 0.72 & 0.72 & 0.72 & 0.72 \\ Xception & 0.77 & 0.77 & 0.77 & 0.77 \\
**JAN + MobileNetV2** & **0.89** & **0.89** & **0.89** & **0.89** \\ OpticNet-71 & 0.80 & 0.80 & 0.79 & 0.80 \\ \hline \hline \end{tabular} Bold represents model with best results.  
\end{table} 
Table 8: Test results for original-size models trained on OCT2023 dataset. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline
3-layer CNN & 0.54 & 0.58 & 0.53 & 0.51 \\ VGG16 & 0.70 & 0.75 & 0.70 & 0.70 \\ InceptionV3 & 0.65 & 0.65 & 0.65 & 0.64 \\ Xception & 0.66 & 0.67 & 0.66 & 0.66 \\
**JAN + MobileNetV2** & **0.89** & **0.89** & **0.89** & **0.89** \\ OpticNet-71 & 0.80 & 0.80 & 0.79 & 0.80 \\ \hline \hline \end{tabular} Bold represents model with best results.  
\end{table} 
Table 9: Test results for models converted to TensorFlow Lite. In the case of the Xception network, both accuracy and F1-score decreased by 11%. Analyzing the confusion matrix in Figure 6d, it can be observed that the greatest number of correct predictions were observed for the VMT classes and the fewest for the DRUSEN class. It is worth noting that before conversion, the DME class obtained the smallest number of correct predictions, the DME class was 11.5%. Figure 6: Confusion matrices for reduced models (Tensorflow Lite). correct predictions, while after conversion to the TensorFlow Lite format, the number of correct predictions increased. The results before and after conversion for the Joint Attention Network + MobileNetV2 and OpticNet-71 networks remained the same. Such results may stem from the architecture of these artificial neural networks. Like other networks, OpticNet-71 recognizes VMT disease with the greatest accuracy while having difficulty distinguishing between the NORMAL and DRUSEN classes, as seen in the confusion matrix in Figure 6f. The confusion matrix for Joint Attention Network + MobileNetV2 shown in Figure 6e confirms the best classification performance obtained with this model.