## 3 Results
### Experiments and Discussion

Table 5: List of the results based on combinations of features extraction methods from Section 2.2 and the classification models from Section 2.3 evaluated over a weighted \(F_{1}\) score based on benign and malignant classifications. Firstly, the methods performance varied between 0.61 and 0.84 in terms of the \(F_{1}\) score for Malignancy. The \"At Least One\" method achieved poor performance due to prediction errors over the \"benign\" class. This problem can be solved by the use of a dynamic activation threshold for decisions to minimize the risk of false-positives, at the cost of resulting in an ethical consideration of this method in the clinical context. Secondly, the methods based on the score are almost the same and varied from 0.76 to 0.83 for the \(F_{1}\) score for Malignancy. By contrast, with these results, the standard deviations remained reasonable, varying from 0.03 to 0.06. Finally, MIL was also evaluated, and a substantial difference was noted between the SIL and the MI-SVM. Indeed, the SIL assumption yielded similar results with the decision based on the \"At Least One\" method, due to an insufficient ability to discriminate on the same \"benign\" class. By contrast, the MI-SVM yielded a number of good results, with an \(F_{1}\) score of 0.82. Both methods are stable, with a deviation that only varied from 0.02 to 0.04. Poor results with the \"At Least One\" and the SIL methods can be due to a lack of discriminative information provided by the \"Inception-ResNet\" for these methods. Following previous results, this paragraph discusses in detail the results of supervised \"Dynamic\" decision threshold and MIL based on \"MI-SVM\" methods over the Malignancy (meaning BCC and LM/LMM) and LM/LMM as the cited clinical study does [27]. Table 7 provides \(F_{1}\) score, precision, and recall based on these experiments. As the classification is binary, recall of the positive class refers to the sensitivity and recall of the negative class refers to the Specificity. The \"Dynamic\" method achieves scores of 0.89 \(\pm\) 0.03 sensitivity and 0.75 \(\pm\) 0.07 specificity for Malignancy; 0.88 \(\pm\) 0.04 sensitivity and 0.75 \(\pm\) 0.07 specificity for LM/LMM pathologies. The \"MI-SVM\" method achieves scores of 0.80 \(\pm\) 0.02 sensitivity and 0.84 \(\pm\) 0.05 specificity for Malignancy; 0.78 \(\pm\) 0.07 sensitivity and 0.84 \(\pm\) 0.07 specificity for LM/LMM pathologies. The \"Dynamic\" method provides more emphasis on sensitivity while \"MI-SVM\" provides a good specificity. These methods are quite comparable to the evaluation of the dermatologists, reaching 0.80 of sensitivity and 0.81 of specificity, but less homogeneous compared to them. Finally, Figure 5 provides receiver operating characteristic (ROC) curves for both malignancy and LM/LMM pathologies on \"Dynamic\" and \"MI-SVM\" methods. In the context of Malignancy evaluation, the measured AUC is 0.89 for \"MI-SVM\" and 0.88 for \"Dynamic\". For LM/LMM evaluation, the measured AUC is 0.88 for \"MI-SVM\" and 0.87 for \"Dynamic\". In the same context of LM/LMM lesions, the experts obtained an AUC score of 0.89, so close to the previous two methods. Apart from this, Figure 6 provides some misleading images: the RCM images in the center belongs to the same patient (image c and d) with similar patterns and homogeneous information while the RCM images on the outside parts of the figure contain hair, artifacts, tricky patterns or nonhomogeneous information (image a, b, e, and f). Also, the images on the bottom left and the bottom right (image b and e) of the figure are examples of images were experts will use stacks of images to make their decision and where the currently developed methods only use a single image.