# Deep Learning Methods for Bug Bite Classification: An End-to-End System
## 3 Results
### Bug Bites Classification Results  
**MobileNet-v2. ** This section represents the results achieved by the pre-trained MobileNet-v2 on the test dataset. Table 10 summarizes the detailed results, including the precision, recall and F1-score, for each class, as well as global metrics, such as accuracy and the macro-average and weighted average of precision, recall, and F1-score. As observed from the table, regarding the class-wise results, this model has the greatest precision, recall, and F1-score for classifying chigger bites. Conversely, the lowest precision, recall, and F1-scores of the model are for classifying non-bite images. As for the global measures, the accuracy of the model and the weighted precision and recall are 73%, while the weighted F1-score is 72%. The macro-average calculated for the precision and F1-score is 73%, while for the recall, this value is 74%. Figure 11 represents the normalized confusion matrix of the classification results on the test dataset. As is evident from the figure, chigger bites are perfectly classified. In the case of bed bug bites, 95% of the images were correctly classified as bed bug bites. Next are the mosquito and spider classes, for which the MobileNet-v2 = based model correctly classified 82% and 83% of images of the test set, respectively. Considering the percentage of correctly classified bug bites, the model performs badly for the tick and ant bite classes, as well as the bee sting class (68%, 67%, and 59%, respectively), whereas the worst performance is seen in the case of images with no bites at all (54%). Figure 10: Model deployment. Observing the confusion matrix in Figure 11, we can also notice that images with no bites are most likely to be confused with spider bites (18% of the images), while those with flea bites are mistaken for tick bites (13%). Some of these results may stem from the characteristics of the network. MobileNet-v2 is also reported to have poor performance in the multiclass classification on a similar dataset of skin lesions in [10]. We believe that this is due to the shallowness of the network, as we comment in the Section 4. However, this should be confirmed with future work. **VGG16. ** The results achieved by the VGG16 model on the test dataset are reported in Table 11. We obtained the greatest precision, recall, and F1-score for classifying mobile images (88%). On the other hand, the lowest precision, recall, and F1-scores that were obtained relate to tick bites (65%) and chigger bites (48% recall and 58% F1-score). Considering the global measures, the accuracy of the model is 76%, whereas the macro-average precision is 77%, and the macro-average recall and F1-score share the same value of 76%. The weighted average of all the metrics, namely, the precision, recall and F1-score, calculated for the VGG16-based model is 76%. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Label** & **Precision** & **Recall** & **F1-Score** \\ \hline Ant bites & 0.78 & 0.67 & 0.72 \\ Bed bug bites & 0.69 & 0.95 & 0.80 \\ Bee stings & 0.73 & 0.59 & 0.66 \\ Chigger bites & 0.86 & 1.00 & 0.92 \\ Flea bites & 0.73 & 0.61 & 0.67 \\ Mosquito bites & 0.79 & 0.82 & 0.81 \\ No bites & 0.58 & 0.54 & 0.56 \\ Spider bites & 0.71 & 0.83 & 0.77 \\ Tick bites & 0.69 & 0.68 & 0.68 \\ \hline
**Accuracy** & & & 0.73 \\
**Macro-average** & 0.73 & 0.74 & 0.73 \\
**Weighted average** & 0.73 & 0.73 & 0.72 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Evaluation metrics for MobileNet-v2 model. Figure 11: Normalized confusion matrix for MobileNet-v2. We obtained a similar conclusion from the normalized confusion matrix, depicted in Figure 12. As we can see from Figure 12, the best-classified class is the flea bites class (90%), followed by the class containing images with no bites (88%) and ant bites (87%). The VGG16-based model performed the worst on the chigger bites class--only 48% of chigger bite images were correctly classified. In fact, they were most likely to be misclassified as bed bug bites (32%). **Inception-v3.