## 1 Introduction  
Based on GLOBOCAN 2018 data [1], stomach cancer is the sixth most common and the fourth most deadly cancer. Unfortunately, the five-year survival rate of this cancer is about 30% and has not increased since the 1990s. Improving the survival rate of this cancer is important to have a better diagnosis and a better understanding of the early stages of cancer. Thus, a promising way is to focus on pre-cancerous lesions and to use new imaging modalities with supervised learning techniques. Presently, practicing mostly use upper gastrointestinal endoscopy, under white light (WL) or Narrow Band Imaging (NBI), to diagnose stomach pathologies. Recognizing inflammatory lesions is still a hard task as they are often undetectable, especially under white light. Lesions are often too small to be seen at a macroscopic level. Thus, biopsies are performed in a systematic and non-oriented way. Little pieces of the stomach are collected and analyzed _a posteriori_ in a laboratory to make a diagnosis. As the location of the samples is randomly chosen, the result could be different from one location to another and depends on the endoscopist's experience. NBI is a system created by Olympus, made of two or three narrow filters centered on the Hemoglobin absorption peaks, thus showing vascularity darker than the surroundings. The system has been successfully used to diagnose [PERSON]'s esophagus [2] and allows good visualization of specific patterns of chronic gastritis and intestinal metaplasia [3]. The main alternative to NBI is the \"Fuji Intelligent Chromo Endoscopy\" (FICE) system by FUJI [4]. FICE estimates multispectral images, between 400 and 695 nm, with a 5 nm step, from a standard endoscopic image. Then, the practicing can choose to display three images among them in red, green, and blue channels. Ten settings are pre-configured. FICE has been proven to be useful for the detection of complete and incomplete metaplasia [5]. The significant difference between FICE and NBI is that FICE proposes a software improvement with the computation of spectral images, and NBI proposes an optics improvement with specific filters [6]. Fuji recently proposed two new medical systems, the Blue Light Imaging (BLI) and the Linked Color Imaging (LCI). BLI allows better visualization of superficial vascular and mucosal patterns with a sensor sensitivity focused on the Hemoglobin absorption peak at 410 nm [7]. LCI has a specific pre-processed composition of the light and applies image processing techniques to increase the color contrast, thus also improving the delineation of pathologies [8]. Lately, endoscope manufacturers (e.g., FUJI, Olympus, Pentax) greatly improved image resolutions [9]. However, fewer improvements are introduced for the detection of the lesions. A better system is the magnifying NBI [10]. [PERSON] et al. used this system to link the apparition of white and blue crests on the stomach's surface to metaplasia [11]. These systems show that non-conventional imaging, more specifically the use of spectral information, is promising to improve the detection of stomach lesions. Multispectral or hyperspectral systems use reflectance or fluorescence to get meaningful information on tissue composition. In particular, reflectance represents the fraction of light coming back from the tissue as a function of the wavelengths. Fluorescence is the emission of light by tissues in response to a shorter exciting wavelength. The light response can be influenced in a different way by amount and type of each of a different biomolecule acting as chromophore or fluorophore present in the tissue. The overall light signal recorded from the tissue will result from the different effects of various components, and that is the reason for which it depends on the tissue composition. For example, the system made by [PERSON] et al., made of seven bands between 465 nm and 665 nm. The three bands, 645 nm, 545 nm, and 495 nm, have been selected as the triplet that minimizes mutual information.