## Materials and Methods
### Deep Learning  
CNNs are neural networks with convolutional layers followed by dense layers. CNN-based methods outperformed other methods in many computer vision problems. Specifically, accurate methods in A-line classification in IVOCT images [22; 23] are using CNNs. Figure 3: OCT-specific transformations: (**a**) the original image in polar coordinates; (**b**) the image after the transformation based on attenuation coefficient estimation. Therefore, in this work, various original (trained from scratch) and pretrained CNN architectures were tested. Pretrained CNNs are fixed, open-source methods that were trained with natural images. They can be efficient in medical image analysis by fine-tuning their parameters [31]. Fine-tuning the parameters leads to faster convergence during training than training from scratch. Such a pretrained network is AlexNet [32], which consists of five convolutional layers with rectified linear unit (ReLU) activations and three dense layers. Some of its activations are followed by max-pooling layers. Although there are more efficient architectures, AlexNet is still a basis for the development of simple architectures that are more easily trained due to their small number of layers. Having been trained with natural images that are included in large databases, pre-trained networks may not be suitable for medical images that represent the interior of the human body. Besides, resizing images to fit these networks may significantly alter their original representation. More specialized networks are needed for medical image analysis. In medical image analysis, regions are segmented based on their texture. When there is no abundance of images, semantic methods are not efficient. Therefore, images are segmented to fix-sized patches. This introduces the need for CNN architectures with smaller input sizes than state-of-the-art architectures. To analyze CT images of lungs, [PERSON] et al. [33] proposed an original CNN that is specialized in classifying small parts of the images. The main characteristics were: five convolutional and three dense layers. This network could be considered a miniature of AlexNet, but it does not use pooling layers. Inspired by common architectures such as AlexNet and the more specialized miniature network by [PERSON] et al. [33] and after experiments with different CNNs, in this work, we propose the use of a 1-D CNN that takes as input axial-lines with the corresponding attributes (Figure 4. To test the nearly optimal depth of A-lines for classification, the input size varied from 1 \(\times\) 60 \(\times\) 3 and 1 \(\times\) 120 \(\times\) 3 by changing the size of the second dimension by 10 pixels each time. The resulting network we finally propose has input 1 \(\times\) 90 \(\times\) 3. It consisted of 3 convolutional layers, 2 dense layers, and an output layer. The size of the convolutional kernels was small to keep small the receptive field so that high spatial frequencies stimulate the kernels. Their exact size of 1 \(\times\) 4, 1 \(\times\) 2, 1 \(\times\) 10 was chosen after experiments. In these experiments, the number of feature maps in each layer was also examined. The use of only one big dense layer accelerated convergence. A much smaller dense layer followed to capture A-line appearances that could be grouped in few categories. Overfitting was dealt with dropout layers after the dense layer and with L2 regularization (The L2 parameter was set equal to 0.0005). After experimentation with activations, leaky ReLU [34] was selected for the convolution layers. Leaky ReLU activation is more robust when applied to the convolutional layers. The last dense layer was followed by a softmax activation. The Adam optimizer was used to reduce categorical cross-entropy [35]. Hyper-parameter tuning was used to select the learning rate to be equal to 0.001. After 200 iterations without improvement of the training error, the training stopped. With this architecture as a basis, deeper CNNs were examined, and their results were compared.