## 5 Conclusions  
In this study, we have developed an end-to-end CADx system with a state-of-the-art deep learning architecture to classify colorectal polyps between benign and pre-malignant, obtained with three different image modalities (WL, BLI, and LCI). We evaluated our framework on an independent test set of 60 patients and compared the results with the diagnosis of 19 endoscopists (6 experts and 13 novices). Our CADx system was trained with a dataset of 2739 images collected from three different hospitals. Anticipating clinical application, we employed EfficientNetB4, which is a state-of-the art architecture for classification. The network demonstrates excellent performance for our CADx system compared to most common networks found in polyp classification literature. Besides its optimal performance, one of the most noticeable downsides we found during training is that the complex computations for scaling and depth limited the batch size for training. We have opened the path for endoscopists towards combining WL, BLI, and LCI to predict polyp histology, and the results have shown that there is potential to enhance the prediction of individual polyps from several modalities, but further studies should conclude the findings with a broader testing set. Moreover, we have experimented with Grad-CAM to offer endoscopists an interpretable answer of the CADx decisions. In further studies, we will investigate our system performance in real-time colonoscopy. In conclusion, we present a CADx system that could be used in routine colonoscopy to classify benign and pre-malignant colorectal polyps. If the CADx system successfully predicts CRP histology, then potentially diminutive hyperplastic polyps could be left in the colon and the suggested 'diagnose-and-leave' strategy could be applied. The same principle can be applied to diminutive adenomatous polyps as well, which then could be resected without performing histological evaluation following the'resect-and-discard' strategy. Overall, the presented study may allow for improved diagnosis of CRC and decrease the current cost burden of histological examinations.  
Conceptualization, R.F.; methodology, R.F.; software, R.F.; validation, R.F., Q.E.W.v.d.Z., and R.M.S.; formal analysis, Q.E.W.v.d.Z.; investigation, R.F. and Q.E.W.v.d.Z.; resources, Q.E.W.v.d.Z., R.M.S., and E.J.S.; data curation, R.F., Q.E.W.v.d.Z., and R.M.S.; writing-original draft preparation, R.F.; writing-review and editing, F.v.d.S. and PH.N.M.W.; visualization, R.F.; supervision, F.v.d.S. and PH.N.M.W.; project administration, A.A.M.M., E.J.S., F.v.d.S., and P.H.N.d.W.; funding acquisition, Q.E.W.v.d.Z., A.A.M.M., E.J.S., F.v.d.S., and P.H.N.d.W. All authors have read and agreed to the published version of the manuscript.  
This project has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No. 721766.  
Figure 4: Example of misclassification due to a wrong decision in the CADx for the WL modality. In this case, BLI and LCI correctly identified the pre-malignant polyp and the CADx system is capable pf identifying it as such.  
**Acknowledgments:** We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research.  
**Conflicts of Interest:** The authors declare no conflict of interest.