## 4 Conclusions  
This research investigated the classification of malignant tumors and particularly LM/LMM pathologies at the image-level and at the patient-level. Firstly, at the image-level, an analysis was performed of previous research and proposed methods based on Transfer Learning showing that the Inception-ResNet architecture trained on the ImageNet database is quite relevant for classifying RCM images, with a weighted \(F_{1}\) score of 0.82 between benign and malignant labels. Secondly at the patient-level, supervised decision methods were compared to MIL methods based on feature extraction with the Inception-ResNet architecture. On the first hand, the classification of malignancy over patients achieves a weighted \(F_{1}\) score of 0.84 with an AUC score of 0.88 using the supervised method based on a dynamic threshold. It achieves a weighted \(F_{1}\) score of 0.82 with an AUC score of 0.89 using the MIL method based on MI-SVM. On the other hand, the classification of LM/LMM over patient achieves a weighted \(F_{1}\) score of 0.83 (Sensitivity 0.88/Specificity 0.75) with an AUC score of 0.87 using the supervised method based on a dynamic threshold. It achieves a weighted \(F_{1}\) score of 0.80 (Sensitivity 0.78/Specificity 0.84) with an AUC score of 0.88 using the MIL method based on MI-SVM. Both techniques are relevant compared to the evaluation of dermatologists, reaching 0.80 of sensitivity and 0.81 of specificity with an AUC score of 0.89. Furthermore, the supervised method based on the dynamic threshold has more sensitivity that can be relevant in the medical context.  
To conclude, the RCM is an operator dependent technique that can be improved by use of computer-aided diagnosis methods that can be particularly useful to confirm the diagnosis and to help the interpretation of the images for clinicians that are not expert. Further development of these findings should focus on the enhancement of the image classification through feature extraction enhancement, in particular by investigation of fine-tuning of the CNN architecture. In another step, the patient decision should be improved by working on the score meaning through score calibration methods and by rethinking the way the decision is taken as a result of these.  
Investigation, R.C.; Methodology, R.C.; Project administration, F.M.; Resources, J.-L.P. and E.C.; Supervision, A.M. and F.M.; Writing--original draft, R.C.; Writing--review and editing, R.C., A.M., J.-L.P., E.C. and F.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Conseil Regional de Bourgogne Franche-Comte of France and the European Regional Development Fund (ERDF).  
The authors declare no conflict of interest.  
We thank [PERSON] and [PERSON] for their work on the data and the permission granted to exploit it.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AUC & Area under the curve \\ BCC & Basal cell carcinoma \\ CART & Classification and regression trees \\ CNN & Convolutional neural network \\ DEJ & Dermal-epidermal junction \\ ERT & Extremely randomized trees \\ GB & Gradient boosting \\ GGD & Generalized Gaussian distribution \\ GLH & Gray level histogram \\ GLCM & Gray level co-occurrence matrix \\ LM/LMM & Lentigo maligna/lentigo maligna melanoma \\ MIL & Multiple instance learning \\ RCM & Reflectance confocal microscopy \\ RF & Random forest \\ ROC & Receiver operating characteristic \\ SIL & Single-Instance Learning \\ SVM & Support Vector Machine \\ \end{tabular}