# Deep Learning Methods for Bug Bite Classification: An End-to-End System
## 4 Discussion
Therefore, we are not able to conduct a deeper analysis in this direction. The simple 3-layer CNN architecture used in [24] provided the worst results (accuracy of 58.5%). On the other hand, experiments \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Study** & **Dataset Type** & **Dataset** & **Performance Metrics** \\ \cline{2-4}  & **(No. Classes/** & **Size** & \\  & **Bites Classes)** & & **Accuracy** & **F1-Score** \\ \hline [PERSON] et al. [10] & Skin cond. & 6178 (1144 bug bites, & \\  & (8/2) & 891 normal skin, & \\  & & 4143 other skin cond.) & & \\ ResNet50 & & 0.72 & 0.72 \\ MobileNet-v2 & & 0.59 & 0.59 \\ DenseNet121 & & 0.72 & 0.72 \\ InceptionResNetV2 & & 0.68 & 0.68 \\ Inception-v3 & & 0.68 & 0.68 \\ ResNet152 & & 0.71 & 0.71 \\ \hline [PERSON] [21] & Bug bites (2/2) & 61 & \\ ResNet50 & & 1 & - \\ \hline Systems, A.T. [22] & Bug bites (2/2) & 61 & \\ VGG16 & & 0.75 & - \\ \hline [PERSON] [23] & Bug bites (7/7) & 171 & \\ ResNet50 & & 0.59 & - \\ VGG16 & & 0.56 & - \\ Inception-v3 & & 0.51 & - \\ Ensemble model & & **0.88** & - \\ \hline [PERSON] [24] & Bug bites (8/7) & 1301 (1144 bug bites, & \\  & & 157 no bites) & \\ Simple 3-layer CNN & & 0.58 & - \\ Inception-v3 All frozen & & 0.68 & - \\ Inception-v3 Unfreeze & & 0.78 & - \\ last 1/3 before tuning & & 0.81 & - \\ Inception-v3 Unfreeze & & 0.81 & - \\ last 1/3 after tuning & & **0.86** & **0.86** \\ \hline
**Current study** & Bug bites (9/8) & 3026 (**1272 bug bites,** 154 & \\  & & no bites, 1600 non-skin) & \\
**MobileNet-v2** & & 0.73 & 0.73 \\
**VGG16** & & 0.76 & 0.76 \\
**Inception-v3** & & 0.77 & 0.77 \\
**DenceNet** & & 0.78 & 0.78 \\
**Ensemble model** & & **0.86** & **0.86** \\ \hline \hline \end{tabular}
\end{table} 
Table 16: Performance comparison of the proposed ensemble scheme individual CNN models examined in this research (MobileNet-v2, VGG16, Inception-v3, and DenseNet169) with the related work. based on the Inception-v3 model provided comparable results to the ones in our study based on the same model and even better in the case when the last 1/3 of the layers were unfrozen after tuning. If we analyze the results more deeply, we can note that the lowest percentage of correctly classified images in [24] is related to the chigger and bed bug bite classes. The model in [24] is most likely to misclassify chiggers (as bed bugs) or bed bugs (as chiggers). When analyzing our results obtained from the Inception-v3 model, the situation is very similar. Additionally, the second-worst percentage of correctly classified images in our research based on the Inception-v3 model is related to the bee sting class, which was not included in [24]. We believe that this has a significant influence and may explain the slightly worse results provided by our customized Inception-v3 model compared to the best-case scenario of the same model in [24]. We hope to obtain more insight on this in our future work. Our proposed ensemble stacking model outperforms all the individual models not only in our study but also in [10; 24]. However, several interesting conclusions can be drawn when we compare our results with those from [10]. In fact, if we make a model-wise comparison, we can note that our pre-trained fine-tuned individual models show higher accuracy and average F1-score in comparison to all the individual models used in [10]. The same applies regarding the comparison of the corresponding models. It is interesting to note that in both studies, ours and [10], the MobileNet-v2 model provided lower values of the performance metrics (more significant in [10] and less significant in our research). We believe that this is due to the shallowness of the network. Similarly, the DenseNet architecture (together with ResNet50 in [10]) outperformed the other individual models in both studies. We believe that the possible explanation for this lies in the fact that DenseNet has a higher capacity for multi-layer feature concatenation and better feature use efficiency [51].