## 3 Results  
CNV screening evaluation, reported in Table 1 and Figure 3, shows that CNV detection on OCTA images achieves the best performance with a precision of 96%, recall of 90%, F1-score of 0.93 and an accuracy of 89.74%. In addition, ROC-AUC and PRC-AUC are 0.99 each. No AMD (healthy CC) OCTA images are also well classified by the proposed VGG19 modified model with a very good accuracy of 94.87% and F1-score of 0.90 (precision 0.85, recall 0.95). Regarding intermediate AMD (drusen/CC impairment) class, the images are, in some cases, confused with no AMD class. Sixty-three images from the intermediate AMD test dataset are correctly classified while 12 images are predicted as no AMD images and 3 as CNV images. This is summarized by the confusion matrix in Table 2. \begin{table}
\begin{tabular}{c c c c c c c} \hline  & **Accuracy (\%)** & **Precision** & **Recall** & **F1-Score** & **PRC-AUC** & **ROC-AUC** \\ \hline HCC & 94.87 & 0.85 & 0.95 & 0.90 & 0.97 & 0.99 \\ DCCI & 80.77 & 0.85 & 0.81 & 0.83 & 0.94 & 0.97 \\
**CNV** & **89.74** & **0.96** & **0.90** & **0.93** & **0.99** & **0.99** \\ \hline \end{tabular}
\end{table} 
Table 1: CNV screening performance of the modified VGG19. Accuracy, precision, recall, F1-score, area under the curve (AUC) for precision-recall (PRC) and receiver operation characteristic (ROC) curves for the three classes of OCTA images (HCC: healthy choriocapillaris, DCCI: drusen/choriocapillaris impairment, CNV: choroidal neovascularization). The overall classification accuracy using transfer learning on our modified VGG19 is 88.46% with a loss of 0.089, while the overall classification accuracy using original VGG19 is 83.76% with a loss of 0.37. The proposed approach on the modified VGG19 achieved better performance than original VGG19 for CNV detection. This is reported in classification reports (Tables 1 and 3) and confusion matrices (Tables 2 and 4) where CNV screening accuracy is 74.36% for original VGG19 against 89.74% for our proposed approach. In addition, PRC-AUC and ROC-AUC are 0.99 each using our modified VGG19, while PRC-AUC is 0.95 and ROC-AUC is 0.97 using original VGG19. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Ground Truth Class**} & \multicolumn{4}{c}{**Predicted Class**} \\ \cline{2-5}  & **CNV** & **DCCI** & **HCC** \\ \hline CNV & 70 & 7 & 1 \\ DCCI & 3 & 63 & 12 \\ HCC & 0 & 4 & 74 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Confusion matrix of the modified VGG19 prediction on a balanced test dataset of 234 OCTA images (78 images for each class). CNV: choroidal neovascularization, DCCI: drusen/choriocapillaris impairment, HCC: healthy choriocapillaris. Figure 3: Receiver operating characteristic (ROC) and precision-recall (PRC) curves for the three classes: advanced wet AMD (CNV), intermediate AMD (drusen/CCI: choriocapillaris impairment), no AMD (healthy choriocapillaris). Area under the curve (AUC) values for each class. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Accuracy (\%)** & **Precision** & **Recall** & **F1-Score** & **PRC-AUC** & **ROC-AUC** \\ \hline HCC & 97.44 & 0.84 & 0.97 & 0.90 & 0.96 & 0.99 \\ DCCI & 79.49 & 0.75 & 0.79 & 0.77 & 0.83 & 0.93 \\
**CNV** & **74.36** & **0.97** & **0.74** & **0.84** & **0.95** & **0.97** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: CNV screening performance of the original VGG19. Accuracy, precision, recall, F1-score, area under the curve (AUC) for precision-recall (PRC) and receiver operation characteristic (ROC) curves for the three classes of OCTA images (HCC: healthy choriocapillaris, DCCI: drusen/choriocapillaris impairment, CNV: choroidal neovascularization). Figures 4 and 5 display Grad-CAM visualizations for correct predictions and incorrect predictions of the three classes, respectively, (no AMD, intermediate AMD, and advanced wet AMD). Grad-CAM heatmaps are superimposed on original OCTA choriocapillaris images with warm colors (red, orange, and yellow) for discriminant features and cold colors (blue, cyan, and green) for non-discriminant features.