## 4 Discussion and Conclusions
The most difficult step of this classification problem is, on one hand, the detection of some confusing CNV where a small, indefinite vascular shape is visible and for which very small amount of OCTA images are available in our training dataset. On the other hand, the discrimination of early small drusen from healthy choriocapillaris remains problematic. These difficulties are addressed in Figure 5 that illustrates two misclassified CNV images (Figure 5J,J), a misclassified intermediate AMD image (Figure 5K) and a misclassified no AMD image (Figure 5L). Image I is misclassified as intermediate AMD with 0.84 of probability and image J is the only CNV image of our test dataset misclassified as no AMD with 0.90 of probability. Feature attribution visualization for these two misclassified images illustrated by Grad-CAM heatmaps in Figure 5 helps understanding the CNN prediction for these cases. The Grad-CAM heatmap of image Figure 5I shows that discriminant features are those of drusen by only black nonflow regions which explains the CNN predicted high probability attributed to intermediate AMD class rather than CNV class. Only one small CNV is visible in Figure 5I and is considered as non-discriminant feature. The CNN prediction is thus based on the most present features on the image. Such images are also ambiguous for clinicians and additional imaging modalities are needed to establish a clear diagnosis. The Grad-CAM heatmap of image Figure 5 explains again the CNN misclassification. Only grainy regions present throughout the whole image are considered as discriminant features by the CNN. The tiny CNV membrane visible on this image is considered as non-discriminant as it is hidden in the grainy texture. This explains the CNN's prediction with high probability (0.90) for image Figure 5J, attributed to no AMD class. To overcome these classification errors, we should supply our training dataset with more ambiguous OCTA choriocapillaris images such as images I and J of Figure 5 to train the model to detect tiny CNV membranes when they are hidden and confused with drusen or CC grainy texture. Drusen or significant flow impairment on OCTA choriocapillaris images appear as nonflow areas/flow deficits that are generally surrounded by the grainy texture of CC. When these areas are small and less important than the CC texture, the OCTA image is considered as ambiguous and confusing even by clinicians. This is the case of the 12 OCTA images misclassified by the CNN and predicted as no AMD images. Early small drusen are hardly visible on OCTA images and can be considered as flow deficits related to OCTA image acquisition process. Thus, the early small drusen manually classified as intermediate AMD images are, in some cases, predicted as no AMD by the CNN. These cases are illustrated by typical images in Figure 5K,L. The above analysis is supported by the predicted probabilities for images K and L of Figure 5. Figure 5K represents an intermediate AMD image predicted as no AMD by the CNN with close probabilities for both classes (0.59 for no AMD against 0.41 for intermediate AMD). Similarly, Figure 5L is a no AMD image predicted as intermediate AMD by the CNN with close probabilities (0.58 for intermediate AMD against 0.42 for no AMD). This last result reveals again the CNN's ability to show uncertainty in cases of confusing features. To resolve this ambiguity, it would be relevant to classify these images as uncertain images for which the clinician should use additional information from other imaging modalities or patient history to decide and make a diagnosis. Despite these few misclassification errors, the CNN showed a great ability to screen and detect CNV on OCTA choriocapillaris images. This main finding is achieved through the transfer learning approach that is used to train the proposed VGG19 modified model to overcome the limitation of the small amount of training data.