## 4 Discussion and Conclusions
This work investigates the application of an Xception deep learning model for the automatic classification of colon polyps from murine (rat) samples acquired with OCT imaging. The developed database is accessible upon request and is part of a bigger database in the process of being published. A strategy for processing B-scan images and extracting regions of interest was proposed as a data augmentation strategy. Test time augmentation strategy implemented with the aim of improving model prediction was evaluated. In addition, this work also aims to compare the differences in the diagnosis capacity of the proposed method when evaluated using B-scan images and C-scan volumes, and for this purpose different clinical metrics were compared. The trained model was evaluated 6 times using different training, validation, and testing sets to provide an unbiased diagnosis of the results. In this sense, we got a model with mean 0.9695 (\(\pm\)0.0141) sensitivity and mean 0.8094 (\(\pm\)0.1524) specificity when diagnosis was performed over individual B-scans, and mean 0.9821 (\(\pm\)0.0197) sensitivity and mean 0.7865 (\(\pm\)0.205) specificity when diagnosis was performed in the whole C-scan volume. Considering the future application of a deep learning method to assist clinical diagnosis with OCT, and in view of the results of this work, successful diagnosis can be achieved both on B-scan images and C-scan volumes. The evaluation of the lesion over a C-scan volume was preferred over the evaluation of an individual B-scan image, so the prediction could be more robust. However, this will not be possible most of the time in the daily clinical routine, for example during patient colonoscopy examination, where in vivo real-time information is necessary for diagnosis and in-situ treatment decision. In this sense, clinical procedures based on the accumulative predictions of various B-scan images could be defined to facilitate clinicians' decision-making during examination. The promising results with the proposed approach suggest that the implemented deep learning based method can identify the clinical features reported in previous clinical studies on the OCT images, and more importantly, that the amount of data and features present on the images database are enough to allow automatic classification. These results are part of ongoing work that will be further extended; however, it has been demonstrated that deep learning-based strategies seem to be the path to achieve the \"optical biopsy\" paradigm. Raw interpretation of new imaging modalities is difficult for clinicians but assisted by an image analysis method, the interpretation can be eased and the reliable diagnosis suggestion can facilitate the adoption of the technology. Consequently, the CADx market can benefit from this progress in the short term as the latest market forecast studies suggest. This work will be further extended and tested with a larger and more balanced version of the murine dataset collected. More sophisticated models accepting larger image size will be tested to check whether classification is improved. Optical properties of the different lesions will be studied in detail with the aim of finding scattering patterns for each type of lesion. OCT volumetric (C-scan) information will be also studied in further detail to make the most of it analyzing both the cross sectional and en-face views. Conceptualization, C.L.S., J.B., and J.F.O.-M.; methodology, C.L.S., A.P., and E.T. ; software, C.L.S. ; validation, C.L.S., J.B., N.A.d.R., and N.A. ; formal analysis, C.L.S., A.P., and E.T. ; investigation, C.L.S. ; resources, J.B., J.F.O.-M., E.G., O.M.C., N.A.d.R., and N.A. ; data curation, C.L.S., J.B., J.F.O.-M., N.A.d.R., and N.A. ; writing--original draft preparation, C.L.S., J.B., and J.F.O.-M.; writing--review and editing, C.L.S., J.B., J.F.O.-M., A.P., E.T., E.G., and O.M.C. ; visualization, C.L.S. ; supervision, E.G. and O.M.C. ; project administration, C.L.S., A.P., and E.G. ; funding acquisition, C.L.S. and A.P.