## 1 Introduction
These three bands are then used to create an enhanced RGB image for better visualization. The system has been tested for ulcer detection [12]. Visualization helps to detect pathologies, but the interpretation remains in endoscopists' hands. The diagnosis thus depends on the endoscopist's experience and is not necessarily correlated with histological results. It is essential to improve the detection thanks to signal and image processing methods adapted to non-conventional modalities. For example, [PERSON] et al. used a hyperspectral camera between 400 and 800 nm to characterize the spectral response of resected tumors [13]. 14 patients have been studied by selecting spectra on healthy and cancerous areas. [PERSON] et al. have conducted similar works. They also used a hyperspectral camera in the range [400, 800 nm] to analyze resected stomachs. They noticed a high inter-patient variability that led them to normalize all spectra according to the mean spectra taken on healthy areas. These works are promising but are all conducted ex vivo. A few works considering in vivo exploration with multispectral systems exist in the literature. In 2017, [PERSON] et al. demonstrated that multispectral data was useful for the detection of adenocarcinoma [14]. Their system is composed of a standard endoscope and a modified light source. The spectral range used in this study is [438, 628 nm]. They have tested several classifiers including Random Forest, RobustBoost, Adaboost or Support Vector Machine (SVM). Their classification accuracy was around 60%. [PERSON] et al. worked on a confocal microendoscope with two modes. One with a gray-level image to observe the fluorescence response and the other to obtain a multispectral image of fluorescence [15]. An attractive aspect of hyperspectral acquisitions is the link between spectra and biological parameters. A spectrum is the sum of several chromophores' contributions. Thus, an unmixing process can be done. For example, [PERSON] et al. characterized gastric mucosa with hyperspectral two-photon microscopy. They obtained images at cellular levels. Unmixing was performed to distinguish four tissue types: epithelium, _lamina propria_, collagen, and lymphatic tissue [16]. [PERSON] et al. acquired spectra via Raman spectroscopy [17]. Spectra were also unmixed to find the relative contribution of DNA, proteins, lipids, and glycoproteins. Relative contributions were successfully used to classify four tissue types: healthy, intestinal metaplasia, dysplasia, and adenocarcinoma. They have obtained a good sensitivity between 80% and 90% and a specificity higher than 90% with a Partial Least Squares-Discriminant Analysis PLS-DA). [PERSON] et al. developed a system with a filter wheel for acquisitions at six different wavelengths. The principle drawback is that images must be registered. A \"Nearest Neighbors\" classifier and an SVM classifier (with a linear or a gaussian kernel) have been used on 5x5 patches to detect pre-cancerous states in the stomach [18]. The best results have been obtained with the SVM classifier with a gaussian kernel with an accuracy of 77%. Except for this work, we rarely find other papers related to in vivo stomach exploration. Other techniques have been developed to help the detection of pathologies with imaging systems. For example, contrast agents can be used to characterize the stomach wall like chromoendoscopy [19]. Some agents are absorbed by cells or accumulate on peaks and valley of the mucosa, thus showing the structure of the cells. The use of contrast agents is not mandatory. Some biological substances can be stimulated by light, acting as fluorophores. This phenomenon is exploited by fluorescence techniques: by exciting fluorophores with at a certain wavelength, they re-emit light in a higher wavelength [20]. In practice, collagen is the most important in sub-mucosa but we can also find Pyridoxal phosphate, riboflavin, phospholipid and porphyrins I and II (PPS) [21]. To the opposite, Chromophores (Hemoglobin for instance) does not re-emit light but absorb it.