# Deep Learning Methods for Bug Bite Classification: An End-to-End System
## 4 Discussion
In most models and classes, there is a significant amount of variation in the precision and recall measures (Figure 18). To analyze the performance of the examined models more comprehensively, it is more suitable to observe the F1-score, which is a combination of these two measures and summarizes the model's performance in a more balanced way. Using the F1-score, it can be noted that the best results are generally achieved by the stacked model, which is to be expected. However, if we exclude that model, it can be seen that different models are the best performers in different classes. The VGG16 model stands out in the ant bites, bee stings, and no bites classes. MobileNet-v2 performs better than other models in the bed bugs and chigger bites classes, Inception-v3 shows excellent results for no bites and tick bites, and DenseNet169 shows the best results in the chigger, mosquito, spider, and tick bites classes. Another thing that is interesting to note here is the big difference in precision and recall in some of the models for some of the classes, such as VGG16 and Inception-v3 in chigger bites or MobileNet-v2 in bed bug bites. This means that these models correctly  
Figure 17: Global evaluation metrics for each model. guess the instances for the target class, but they classify a large part of the instances of other classes as that class. Additionally, there are instances that should be classified in that class but they are miss-classified. We are unable to make a valid and fair comparison of the results primarily due to the lack of published research on the classification of images of bug bites. In the few web sources that address some aspects of this problem, the datasets used to evaluate their strategies are different, both in structure and size. However, in Table 16, we summarized the achievements of the other authors together with the results gained in this research in order to be able to provide a rough comparison. As we can see in Table 16, the dataset on which we performed the analysis is the largest regarding the number of bug bite images and the most comprehensive regarding the types of bug bites (number of classes). Since our mobile application enables entering the newly captured images into the database, significant enrichment of the dataset is additionally expected. A reduced number of bug bites images but still of comparable size is considered in [10; 24]. The limitation of the study in [10] regarding the dataset is that they only considered two classes of bug bites, thick bites, and general insect bites. On the other hand, Ref. [24] lacks consideration of a bee bites class, and, furthermore, does not include non-bug bite images. In the sources [21; 22; 23], the examination was performed on a significantly smaller data set. Figure 18: Model performance for each class. Evaluation metrics: precision, recall and F1-score. Each of the nine graphs refers to each of the classes in the dataset. Regarding the performance metrics, [21; 22] provide an example of the application of the ResNet50 and VGG16 architectures for bug bite classification on a very small dataset (61 images in total), consider only mosquito and tick bites, and test their performance with only four test images. They obtain 100% and 75% accuracy, but we can not draw any reliable conclusions based on these sources. In [23], in comparison to our research, the authors obtained significantly lower accuracy based on the same individual models, VGG16 and Inception-v3, evaluated on a dataset of only 171 images. We believe that the reasons for this are the more appropriate customization and implementation of the fine-tuning strategy for the examined models in our study. On the other hand, the reported accuracy in the same research on the basis of an ensemble model is slightly higher (88%) than the one we obtain with the proposed ensemble stacking model (86%). However, they do not support this obtained value of the accuracy with any other performance metric and they do not give any discussion and details about the model.