## 3 Results
### Classification Tasks
#### 3.2.2 Deep Learning  
As mentioned in Section 3.1.2, deep learning implies the use of deep neural networks, and typically CNNs for image analysis. CNNs can automatically learn high-level features from the input image and therefore have the advantage of not requiring the extraction of hand-crafted features for classification [88], simply needing the input image and the correct class to which it belongs. The drawbacks of deep learning for classification are the same as those mentioned for segmentation tasks in Section 3.1.2. An advantage that classification tasks have over segmentation tasks when considering deep learning is the fact that it is typically less painstaking to obtain the expert ground truth, since manual segmentations can be very time consuming and require the usage of basic image processing software whereas manual classification of images is usually quicker and easier. For OCTA image classification, deep learning methods were employed for artery/vein classification [78], DR detection [86; 89; 90], AMD detection and staging [91], and chorioretinopathy detection [92]. The architectures that were employed included UNet [48], VGG16 and VGG19 [53], ResNet50 [56], and DenseNet [93]. All of the networks took as input a 2D image, with the exception of the work by [PERSON] et al. [91] that did not use the 3D acquired volume but stacked 2D images of the retinal layers of interest, obtaining a 93.4% testing accuracy at binary classification of neovascular AMD vs. non-AMD. Deep learning methods were employed in many clinical applications of classification tasks: DR classification, AMD classification, artery/vein classification, and Central Serous Chorioretinopathy (CSC) classification. [PERSON] et al. [92] presented a deep learning method based on a VGG16 pretrained model for CSC classification and obtained a final accuracy of 95%. For artery/vein classification, [PERSON] et al. [78] used a fully connected network based on the UNet for classifying 30 DR and 20 healthy images, obtaining an accuracy equal to 86.75%, showing lower performances than those presented by the same authors [42] using a machine learning technique (accuracy = 96.57%). When considering AMD classification, [PERSON] et al. [91] presented an interesting study employing a custom-made 3D CNN and using as input a stack of 2D images of retinal layers of interest. When using a two-class classification (i.e., NV-AMD vs. healthy), the classification accuracy was quite high (93.4%), but when considering a three-class classification (NV-AMD vs. non-NV-AMD vs. healthy), the accuracy decreased (77.8%). For DR classification, numerous approaches were presented, and the most promising was the study by [PERSON] et al. [90] that used a densely and continuously connected neural network with adaptive rate dropout. The obtained accuracy was equal to a maximum of 96.5% for two-class classification and minimum 67.9% considering a four-class classification. Another study to note is the one by [PERSON] et al. [86] that employed an Ensemble network and obtained an accuracy equal to 92 \(\pm\) 1.92%. Higher accuracy values were obtained using a machine learning method [33]; however, it must also be pointed out that the databases in the deep learning methods are also almost double or triple in size.