## 1 Introduction
In addition to previous studies, the calculation of the angular spectrum of the scattering coefficient map has also revealed quantifiable variances on the different tissue types [26]. The clinical characteristics of the lesions that can be observed on the OCT images can be further exploited by image-based analysis. Image and signal processing methods can allow dealing with the noisy nature of the signal, whereas machine learning algorithms are able to exploit the spatial correlation of the biological structures to make the most of them. These types of algorithms can detect, and quantify, subtle variations on images that the naked human eye cannot and can be applied with the goal of performing automatic interpretation of the images for image enhancement, lesion delimitation, or classification tasks. However, as seen in previously reviewed studies, few attempts of applying these methods for colon polyps on OCT images have been found, showing that there are opportunities of research in the area. The main limitation of traditional machine learning methods is the need to process the original data from their natural form to another form of representation appropriate for the targeted problem. Image processing methods must be carefully applied to extract the most representative features of the data, aiming to resemble how the experts analyze the images. Then, the extracted features are passed as input to the selected classifier method. Unlike deep learning approaches, traditional machine learning methods require tailored feature extraction which is followed by a shallow machine learning method. This makes them less prone to generalization and leads to lower discriminative power [27]. Under the deep learning paradigm, image feature extraction and classification are simultaneously performed through a network architecture representing all possible solution domains and which is optimized by means of a loss function minimization that seamlessly drives the network parameters towards a suitable solution. Convolutional neural networks (CNN) [28; 29] have surpassed classical machine learning methods [30; 31], and even medical expert capabilities [32; 33; 34]. They have been also successfully applied in colon cancer histopathological classification [35; 36], MPM classification [37], polyp detection on colonoscopy [38; 39; 40], or histological colon tissue staining [41]. The application of deep learning methods to OCT medical images is a recent trend and only few examples of application are available. Ophthalmology being the oldest context of application of OCT, most examples are found in this area, and some others in cardiology and breast cancer [42; 43; 44; 45]. In gastroenterology of the lower track (colon), only one recent work has been identified [46]. A pattern recognition network called RetinaNet [47] has been trained to distinguish normal from neoplastic tissue with a 1.0 sensitivity and 0.997 specificity. The success of the model is based on a dentate structural pattern, identified in normal tissue in previous studies, being utilized as a structural marker on the images used as input during training and evaluation. In this sense, the B-scan images on the dataset (26,000 images acquired from 20 tumor areas) are manually inspected to identify \"teeth\" samples representing normal colonic mucosa and \"noisy\" samples representing malignant tissue. On evaluation, the network provides a list of boxes where these patterns are found along with the probability, and average scores are calculated over a sequence of N adjacent B-scan images. The drawback of this approach resides in the identification of the \"teeth\" pattern in normal tissue, but no other patterns have been identified for malignant tissue, just assuming that the \"teeth\" pattern does not appear in that case.