# Deep Learning Methods for Bug Bite Classification: An End-to-End System
## 5 Conclusions  
A bug bite can cause serious, sometimes life-threatening conditions. This may require immediate medical attention, which is directly related to the type of bug that is causing the bite. The different manifestations of bug bites and their similarity to other skin diseases make the identification of the bugs a serious challenge even for a medical expert. Hence, the necessity for a computer-aided automated system that can allow the fast and efficient identification of a given bug bite based on a dataset of images of other people's bug bites is evident. This kind of system will certainly help both the affected individuals and medical practitioners. Providing such an important and unique solution was the main goal of our paper. Even though the automatic detection of various skin conditions is an active field of research, to the best of our knowledge, there are limited related results on the topic of bug bite classifications. The known work on this topic covers one or a few simple models applied on smaller datasets with fewer classes and gives no detailed discussion or analysis. Thus, the aim of our paper was to overcome this limitation and provide a comprehensive approach to addressing the problem of the automatic classification of bed bug bites. In this regard, we created a dataset for the evaluation of the proposed strategies, which is the largest in comparison to the other related sources and contains the largest number of bug bite classes. Additionally, we performed a thorough comparison and discussion of the state-of-the-art deep learning architectures, such as MobileNet-v2, VGG16, Inception-v3, and DenseNet169, used with transfer learning and tuned to solve the problem of bug bite classification. We utilized the knowledge gained with this analysis to propose a new ensemble model and significantly improve the classification results. Finally, we developed a unique system for bug bite classification based on images taken with mobile phone cameras, which relies on a two-tier approach. At the first level, skin vs. non-skin images are differentiated on the mobile phone with a very high accuracy of 99%. Bug bite classification is addressed at the second level and is performed only on skin images. According to the obtained results regarding the bug bite classification task, this study showed that a CNN-based approach with transfer learning is applicable to the bug bite classification problem. As can be seen in the results, the examined CNN models performed differently against different classes of bites. Namely, DenseNet169 outperformed the other classifiers regarding the chigger bite, mosquito bite, spider bite, and tick bite classes, Inception-v3 showed the best results for no bites and tick bites, the VGG16 model performed better than other models in the ant bite, bee stings and no bites classes, and MobileNet-v2 stood out in the cases of the bed bug bite and chigger bite classes. In terms of the general evaluation metrics, it should be stressed that among the individual models, DenseNet169 provided the best results, reaching an accuracy of 78%. It should also be emphasized that the best results were generally achieved by the proposed stacked model, proving that we can benefit from the different classification properties of the separate models. These results are based on the general evaluation metrics such as accuracy (86%), macro-average calculated for precision (86%), recall (87%), and F1-score (86%), as well as the weighted average calculated for precision (86%), recall (86%), and F1-score (85%). Considering the fact that we can only refer to a few sources in which the problem of bug bite classification is investigated, we cannot make a fair comparison. However, we can still note that the results gained in this research are better than or comparable to those mentioned in the related sources. Here, we should highlight that the results were achieved on a greater dataset with more bug bite classes.