## 3 Results
#### UMAP Fine-Tuning  
UMAP has several parameters that can be fine-tuned. Some of the most useful ones are the number of neighbors and the size of the local neighborhood used for manifold approximation. Larger values preserve the global view of the manifold, while smaller values affect the local view. In other words, smaller values will result in more but smaller clusters, and larger values will result in fewer but bigger clusters. Other valuable optional parameters are the learning rate used for the embedding optimization, and local connectivity indicating the number of nearest neighbors connected on the local level. The higher this value is, the more connected the manifold becomes locally. The negative samples rate refers to the number of negative samples to select per positive sample in the optimization process. Increasing this value will result in greater repulsive force being applied, and higher optimization cost, but slightly more accuracy. We used fixed numbers of the dimensions, those were two and three, as it is easier to visualize and was sufficient for the task. If selected carefully, the number of epochs or iterations should not affect the learning process too much. However, if we consider extreme high or low values, it might severely deteriorate the generalization. The three-dimensional display of the data after applying UMAP visually shows the clustering results. We show example visualizations for two of the best performing models' embeddings with UMAP dimension reduction after applying a grid-search on the aforementioned parameters. Figure 4 shows UMAP embedding space of DenseNet201 on the training and test set. Figure 5 shows UMAP embedding space of Xception on the training and test set. By careful examination of Figures 4 and 5, it can be seen that the results shown in the subplots (a) and (b) in each of the individual figures are quite similar, almost indistinguishable, which is supported by the hypothesis that the test subset represents the overall dataset distribution. Therefore it will give rise to a relatively accurate estimate of the overall performance. Whereas in the corresponding subplots (a), (b), and (c) from each of the two figures, it is noticeable that the clusters are different between the plots, supporting the idea that different pre-trained models will give rise to a different set of features, leading to a different set of misclassifications between the two pre-trained models. These misclassifications are then detected by employing the ensemble-like methodology. Clusters were separated from each other by means of the graph clustering methods detailed in Section 2.8. Figure 4: UMAP embedding space of DenseNet201 on training and test set. (**a**) shows _cross-section_ and _other_ labeled data from the training set, (**b**) shows _cross-section_ and _other_ labeled data from test set, (**c**) illustrates both labeled train and test data points in the same representation space. DenseNet201 and Xception were selected as follows. We repeated the evaluation with the following architectures: NASNetLarge, Xception, InceptionResNetV2 and DenseNet201. They all had promising results as Tables 1 and 2 show the numerical outcomes. We displayed commonly used evaluation metrics for the classification results. Accuracy summarizes the performance of a classification model, its value shows the amount of correct predictions in the total number of predictions. Precision is the fraction of correct predictions among all the predictions that the model made. Recall (true positive rate) refers to the probability of a positive test, which shows the ratio of correct predictions in contrast to all the possible correct predictions. Specificity (true negative rate) relates to the model's ability to not make incorrect predictions. F1 score is a measurement that is calculated as a weighted average of both precision and recall values. F1 score has its best value at 1 when the model is perfect and worst value at 0 when none of the predictions are correct.