## 2 Materials and Methods
### Misclassification Detection  
In the current study, the aforementioned steps are all it takes to achieve high accuracy for this particular dataset. Nevertheless, our goal was to get a near-perfect accuracy without involving deep learning specialists too much or at all. Consequently, we needed a simple approach to detect misclassifications in the model. We have considered multiple strategies such as identifying out-of-distribution data or anomalies. However, training an anomaly detector is not trivial, there is no guarantee that anomalies will get misclassified or that a misclassification is an anomaly.  
We note that the UMAP model is dealing with the representations of the pre-trained model, so the features captured by the latent space will highly depend on the model architecture and the data used for training that model, such as the ImageNet dataset. With this idea in mind, we hypothesize that different pre-trained networks may result in distinctive features. Training a dimensionality reduction algorithm such as UMAP on these features might result in a different set of misclassifications for different pre-trained networks. Therefore we used more than one network to detect possible misclassifications in the data. We can say that a data point is dubious when two models classify it differently, meaning they disagree on which class the data point belongs to. For the most accurate classification, we selected the two best performing pre-trained models. Table 1 shows the common metrics of the tried algorithms on the test dataset which means in our case DenseNet201 (with 0.9992 accuracy) and Xception (with 0.9976 accuracy) were chosen. The accuracy value refers to the number of correctly classified images in relation to the total number of data instances. It has its best value at 1. More than one network is an ensemble-like approach that can be useful as they address certain issues of learning algorithms that output only a single hypothesis. For example, different networks may be stuck in different local minima of the problem space and a voting method can be advantageous as it could reduce the risk of not generalizing well to unseen data. An example is a recent Kaggle competition: the winner produced state-of-the-art results with 18 different networks combined into an ensemble [34] and larger ensembles were also competitive. For a review on ensemble methods, see, e.g., [35] and the references therein.  
In our case, we can turn to the domain experts if models disagree and, due to the high quality of the clusters, a small number of networks (two in our case) is sufficient.  
Our approach for using more than one network drastically reduces misclassifications by detecting potential errors in the models' prediction. However, the more models introduced, the more clusters need to be labeled by the experts, and each model results in approximately 10-25 groups. More models will give rise to more misclassification candidates and this increases the experts' work in the process. In turn, if the number of such candidates is too high then more elaborate ensemble learning based voting system using  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Specificity** & **Recall** & **F1** \\ \hline VGG16 & 0.9960 & 0.9988 & 0.9975 & 0.9952 & 0.9970 \\ VGG19 & 0.9912 & 0.9988 & 0.9975 & **0.9888** 1  & 0.9935 \\ NASNetLarge & 0.9968 & 0.9976 & 0.9951 & 0.9976 & 0.9976 \\ Xception & 0.9976 & 0.9976 & 0.9951 & **0.9988** 1  & 0.9982 \\ InceptionResNetV2 & 0.9952 & 0.9988 & 0.9975 & 0.9941 & 0.9964 \\
**DenseNet201** 1  & **0.9992** & **1.0000** & **1.0000** & **0.9988** & **0.9994** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Pre-trained Models’ Results on the Test Set.  
more networks can overcome the problem. There is trade-off between the required accuracy, the number of networks, and the amount of manual work that should be done.