## 1 Introduction  
Age-related Macular Degeneration (AMD) is the leading cause of irreversible blindness in the elderly population of developed countries. AMD is characterized by changes in the Retinal Pigment Epithelium (RPE), Bruch's Membrane (BM), or Choriocapillaris (CC) complex [1]. There are several staging systems for AMD, but the most widely used is the AREDS (Age Related Eye Disease Study) classification, distinguishing between early, intermediate and late AMD (see Figure 1) [2]. Early and intermediate AMD are characterized by the presence of drusen and pigmentary changes. Late AMD consists of wet AMD, characterized by choroidal neovascularization (CNV), and dry AMD, characterized by geographic atrophy (GA) in the macular area [3, 4]. While both dry and wet AMD are visually threatening, in the particular case of wet AMD, CNV progression can result in rapidly deteriorating visual acuity, leading to scarring and irreversible visual loss [4, 5]. Moreover, as hallmarks of early and intermediate AMD, drusen precede the progression to late AMD [6, 7]. Hence, distinguishing between the early, intermediate, and late AMD plays a key role in both follow-up and treatment decisions, in order to preserve the visual prognosis. Novel imaging techniques, such as optical coherence tomography angiography (OCTA), contribute to the accurate diagnosis of early, intermediate and late AMD in a depth-resolved and non-invasive manner [8; 9; 10]. Besides the accurate detection of CNV or GA in the CC segmentation [11], OCTA has demonstrated CC alterations, i.e., flow deficits (FD) and signal attenuation associated with drusen, in early and intermediate AMD [12]. Moreover, recent literature has shown that choriocapillaris FD predict drusen enlargement, therefore being a significant risk factor for late forms of AMD [7]. Therefore, earlier diagnosis could ensure better follow-up of patients at high risk of conversion to advanced wet AMD. A computer-aided diagnosis (CAD) tool in ophthalmic clinical routine could be of significant assistance for clinicians at daily diagnosis and during follow-up. The detection of CNV in the context of wet AMD on OCTA images is very challenging due to OCTA various artifacts [8; 9]. Many recent papers dealt with the contributions of artificial intelligence (AI) and deep learning (DL) approaches in ophthalmology [13; 14; 15]. However, only few works have addressed CNV screening on OCTA images using DL methods [16]. Most of the existing works focused on the diagnosis of AMD using OCT or Color Fundus Photography. Moreover, published works on OCTA images involved other retinal diseases such as Diabetic Retinopathy (DR) [17]. In 2017, [PERSON] et al. [18] proposed a CAD system based on a multi-scale convolutional mixture of expert model to identify accurately dry AMD and diabetic macular edema using OCT images. Two different macular OCT datasets of 4142 and 3247 B-scans were used for the training step. A very good classification score of 99.85% was derived from the receiver operating characteristic curve (ROC-AUC). In 2018, [PERSON] et al. [19] used a set of 67,401 color fundus images of AMD patients to estimate 5-year risk of progression to advanced stages of AMD by DL techniques. Two AMD severity scales (4-step and 9-step) were considered, and a human versus machine comparison was carried out. This study achieved weighted k scores of 0.77 for the 4-step and 0.74 for the 9-step AMD severity scales. The same year, [PERSON] et al. [20] exploited a database of 120,656 color fundus images, manually graded in 13 AMD severity levels, to train several CNN architectures (AlexNet, GoogLeNet, VGG, Inception-V3, ResNet and I-ResNet-V2). A very good-weighted k of 92% was obtained. [PERSON] et al. [19] have also shown that deep CNN could be efficient to grade color fundus images in four classes: no AMD, early AMD, intermediate AMD, and advanced AMD. The study included  
Figure 1: Classification of three types of OCTA choriocapillaris images (no AMD, advanced wet AMD, intermediate AMD).