## 4 Discussion and Conclusions  
CNV screening on OCT-angiography choriocapillaris images is challenging due to the high variance in neovascular membranes sizes, shapes, and locations. Additionally, the speckle noise and the image acquisition process makes the detection task even more difficult, particularly to differentiate CNV or early drusen from the grainy texture of the choriocapillaris. Our proposed VGG19 modified model achieved very good CNV detection performance with an accuracy of 89.74% and F1-score of 0.93 (as reported in Table 1 [0.96 of precision, 0.90 of recall]). The ROC-AUC and PRC-AUC were 0.99 each (Table 1 and Figure 3). The confusion matrix in Table 2 shows that the CNN could accurately detect the CNV lesion on 70 images out of 78 independent test images. Seven CNV images are confused with intermediate AMD images, only one CNV image is misclassified as healthy CC, and 3 images of intermediate AMD are misclassified as CNV. Concerning the no AMD (healthy CC) classification performance by our model, the statistical analysis reported in Table 1 and Figure 3 shows that it is the second-best classified class after CNV. Precision and recall are 0.85 and 0.95, respectively, with a ROC-AUC of 0.99, a PRC-AUC of 0.97 and an accuracy of 94.87%. The confusion matrix in Table 2 indicates that 74 no AMD images are correctly classified, only 4 images are confused with intermediate AMD images and no healthy CC image is misclassified as CNV. Finally, for intermediate AMD detection, statistics show a F1-score of 0.83 (precision of 0.85 and recall of 0.81), an accuracy of 80.77%, a ROC-AUC of 0.97 and a PRC-AUC of 0.94 (reported in Table 1 and Figure 3). These results are better explained by the confusion matrix attributions in Table 2, where 63 of the 78 intermediate AMD images are correctly classified, while 12 images are predicted as no AMD and 3 images confused with CNV. Furthermore, the CNN predicted probabilities demonstrate, on one hand, the CNN certainty when predicting correct classes (probability higher than 0.90 for the images in Figure 4A,B,D,E,G,H), and on the other hand, the CNN uncertainty for more difficult cases, such as images C and F of Figure 4. Image C illustrates a flow impairment clustered only on one region of the image and surrounded by the grainy texture of the CC on the whole  
Figure 5: Grad-CAM visualizations for incorrect predictions by the proposed VGG19 modified model. To the right of each OCTA image, the corresponding Grad-CAM visualization of the CNN prediction. (**I,J**) advanced wet AMD (CNV) images, (**K**) intermediate AMD image and (**L**) No AMD (healthy CC) image. image. Therefore, the CNN predicted probabilities for image C are 0.70 as intermediate AMD and 0.29 as no AMD. Regarding image F, the CNV membrane is covered by different drusen surrounded by the grainy texture of the CC. Consequently, the CNN predicted probabilities are 0.63 for CNV and 0.36 for intermediate AMD. This shows the CNN's high ability to discriminate wet AMD eyes from healthy and intermediate AMD eyes and to show uncertainty for ambiguous and confusing cases. The Grad-CAM visualization (see Figure 4) provides a better understanding of these results. No matter the shape, size, and location of the CNV on images of Figure 4E-H; the CNN's predicted high probability is attributed to the correct region with high discriminative CNV features. Conversely, the CNN's low probability is attributed to the non-discriminative regions. Similarly, regardless of the presence of artifacts on images of Figure 4B,C); the CNN feature attribution is correctly highlighted by Grad-CAM heatmaps. This proves that the CNN prediction is based on relevant regions of the OCTA choriocapillaris images related to the three classes: no AMD, intermediate AMD, and advanced wet AMD, that are also considered by the expert reader to detect CNV on OCTA images.