###### Abstract
Alternatively, LCI accentuates color contrast by decreasing the intensity of blue-light and emphasizing red-light, providing better delineation and detection of lesions and inflammation [13; 14]. The increasing development of new acquisition systems and the growing need of in-vivo discrimination between different types of CRPs introduces new challenges onto image interpretability in clinical practice. Each new imaging mode comes with its own learning curve and reveals potential clues that enable in-vivo histology assessment. Computer-aided detection (CADe) and diagnosis (CADx) systems facilitate fast and objective decision-making during clinical assessment, offering medical experts additional information for medical diagnosis. Furthermore, while providing the endoscopist with additional evidence to support the diagnosis, it can also shorten the learning curve for such new modalities by pointing out the relevant visual clues for treatment decisions. Although gastroenterologists are well-trained in identifying suspicious areas in the colon, there has been a rapid increase in the development of CADe systems to help localize CRPs from colonoscopy images. The introduction of deep learning in medical imaging and the increased availability of large datasets, both public and private, have helped to shape CADe systems towards real-time detection. Several studies have proposed end-to-end CNNs to tackle the problem of colorectal polyp detection, which aims to differentiate between normal bowel tissue and polyps. Such work has been explored on several datasets of WL and NBI [15; 16; 17; 18; 19], achieving specificity and sensitivity higher than 90% and successfully locating polyps on video frames. Given the potential of CNN, the next logical step is to observe its capabilities in a real clinical environment. In the study of [PERSON] et al. [20], a CADe system was trained with 8641 WL and NBI polyp images. The CNN was tested on an external dataset with a variety of polyp sizes and achieved an accuracy of 96.1%, with an area under the curve (AUC) of 0.99. Additionally, 3 experts reviewed a set of colonoscopy video frames with and without assistance of the CNN and an increase in polyp detection was observed after employing the CADe system. A similar study was conducted by [PERSON] et al. [21], where an open nonblinded trial was performed on 1058 patients, of which 536 were analyzed with colonoscopy and 522 were subjected to colonoscopy with a CADe system. The system achieved specificity of 95.9% and an AUC of 0.98 on different polyp sizes and types. The study observed an increased performance on polyp detection when the experts were assisted by a CADe system. Overall, in recent years, systems for polyp detection have achieved impressive results and been shown to be an effective tool on assisting medical experts. Assuming a high detection rate, the following issue presented to clinicians is to visually identify a polyp for being benign or pre-malignant, which imposes an extra challenge that several studies have tried to overcome. In early studies, CRPs were classified based on local features from blood vessels using NBI images [22; 23; 24], or exploiting a combination of chromoendoscopy, WL, and NBI [25]. In [PERSON] et al. [26], handcrafted features were used to predict the histology of polyps using Support Vector Machines (SVMs) and clinical classification models. The development of CNNs also had a great impact on colorectal polyp classification. Initial work employed classifiers using features extracted from broadly used CNNs [27; 28; 29; 30], such as AlexNet, ResNet, or InceptionNet. Next, a more traditional classifier such as SVMs was required to classify CRPs between healthy and malignant. The early success pushed the development of several CADx systems to classify polyps using different classification schemes. In [PERSON] et al. [31], high-accuracy results were obtained (sensitivity, 93.0%; specificity, 93.3%) using SVMs on a dataset of 118 lesions obtained with NBI magnifying colonoscopy.