## 3 Results
### Experiments and Discussion  
The results of Section 2.3 regarding image-level decisions were performed only on the labeled images and are listed in Table 5. The most suitable handcrafted extraction method was based on the \"Wavelet\" method combined with the \"SVM-Linear\" model, reaching an weighted \(F_{1}\) score of 0.74 with stable performance of 0.04. In general, all of the handcrafted methods performed in a quite similar and stable way, varying from 0.69 to 0.74 for the \(F_{1}\) score and 0.03 to 0.07 for the deviation. In addition, the Transfer Learning-based feature extraction reached higher scores with the \"SVM-Linear\" model, in particular with the \"Inception-ResNet\" architecture, which achieved a weighted \(F_{1}\) score of 0.82 with a deviation of 0.04. With this same model, the Transfer Learning feature extraction methods varied from 0.76 to 0.82, with a deviation range from 0.02 to 0.04. By contrast, all of these architectures were poorly processed by the \"SVM-RBF\" model and can be explained by the high-dimensionality of the extracted features that resulted in overfitting despite the cross-validation of the regularization term. In this situation, the \"VGG-16\" architecture was more suitable with only 512 features than the remaining architectures providing 1536 or 2,048 features. In regards to tree-based models, the CART model weighted \(F_{1}\) score varies between 0.69 and 0.71 (deviation between 0.03 and 0.05) and 0.58 to 0.64 (deviation between 0.02 and 0.12), respectively for handcrafted methods and transfer learning methods. On the other hand, the gradient boosting (GB) model weighted \(F_{1}\) score varies from 0.67 to 0.73 (deviation between 0.04 and 0.07) and 0.78 to 0.81 (deviation between 0.04 and 0.05). The above two sentences can be explained by an overfit in a high dimensional situation for the CART model and in a low dimensional situation for the GB model. In opposition, the random forest (RF) and extremely randomized trees (ERT) models were homogeneous along with handcrafted and transfer learning features as they are less prone to overfitting in both low and high dimensional feature spaces. To sum up the aforementioned results, the rest of this article retains only the best combination, with \"Inception-ResNet\" as the feature extraction methods and the Linear SVM as the classification model. This paragraph focuses on the methods implemented to reach the patient diagnosis (see Section 2.4) and it relates to the initial RCM data (including unlabeled images) used previously to evaluate specialists [27]. All these experimental results are listed in Table 6 and discussed below. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Classifier Type**} \\ \cline{2-7}  & **CART** & **RF** & **ERT** & **GB** & **SVM - Linear** & **SVM - RBF** \\ \hline
**Haralick** & 0.71 \(\pm\) 0.05 & 0.71 \(\pm\) 0.04 & 0.71 \(\pm\) 0.05 & 0.67 \(\pm\) 0.05 & 0.71 \(\pm\) 0.05 & 0.70 \(\pm\) 0.07 \\
**GLH + GLCM** & **0.71 \(\pm\) 0.03** & 0.71 \(\pm\) 0.03 & 0.72 \(\pm\) 0.05 & 0.67 \(\pm\) 0.07 & 0.70 \(\pm\) 0.05 & 0.72 \(\pm\) 0.04 \\ \hline
**Fourier** & 0.69 \(\pm\) 0.05 & 0.70 \(\pm\) 0.03 & 0.70 \(\pm\) 0.04 & 0.69 \(\pm\) 0.04 & 0.73 \(\pm\) 0.03 & 0.69 \(\pm\) 0.06 \\
**Wavelet** & 0.70 \(\pm\) 0.03 & 0.72 \(\pm\) 0.04 & 0.74 \(\pm\) 0.05 & 0.73 \(\pm\) 0.05 & 0.74 \(\pm\) 0.04 & **0.72 \(\pm\) 0.03** \\ \hline
**VGG-16** & 0.58 \(\pm\) 0.12 & 0.73 \(\pm\) 0.03 & 0.76 \(\pm\) 0.06 & 0.78 \(\pm\) 0.04 & 0.76 \(\pm\) 0.03 & 0.64 \(\pm\) 0.20 \\
**Inception-V3** & 0.63 \(\pm\) 0.04 & 0.74 \(\pm\) 0.06 & 0.78 \(\pm\) 0.04 & 0.79 \(\pm\) 0.05 & 0.79 \(\pm\) 0.03 & 0.44 \(\pm\) 0.04 \\
**ResNet** & 0.62 \(\pm\) 0.08 & 0.75 \(\pm\) 0.05 & 0.78 \(\pm\) 0.02 & **0.81 \(\pm\) 0.05** & 0.79 \(\pm\) 0.02 & 0.43 \(\pm\) 0.05 \\ \cline{2-7}
**Inception-ResNet** & 0.64 \(\pm\) 0.02 & **0.76 \(\pm\) 0.03** & **0.79 \(\pm\) 0.05** & **0.81 \(\pm\) 0.05** & **0.82 \(\pm\) 0.04** & 0.44 \(\pm\) 0.06 \\ \hline \hline \end{tabular}
\end{table}