# A Multiclassification Model for Skin Diseases Using Dermatoscopy Images with Inception-v2
## 5 Conclusions  
This paper proposes a unique multiclassification model for dermatological images based on Inception-v2 and the focal loss function. The model is developed by comparing two different loss functions, NNLLoss and focal loss, and is designed to enhance the classification of dermatological images. The results demonstrate that focal loss enhances the functionality of the model in the case of an uneven dataset distribution. Furthermore, the use of data augmentation is shown to balance the dataset in such cases, while hair removal improves detection and classification. Finally, ablation experiments demonstrate the contribution of each module to the overall model performance. The results show that under the training of the proposed method, the proposed model is able to effectively classify seven types of dermatological images with a peak accuracy of 91.42% and an average accuracy of 89.04%, which provides a good classification performance and can provide important support to dermatologists and health professionals in diagnosing dermatological diseases.  
It should be noted, however, that the dataset used in this paper is the publicly available dataset ISIC 2019. This dataset contains a greater number of images of light-coloured skin and is finite and unbalanced. In subsequent research, we plan to explore further how these limitations of the ISIC 2019 dataset may restrict the performance of our model across individuals with different skin tones and types. We recognise that in order to enhance the generalisation capability of our model, it is necessary to consider a more diverse dataset. Therefore, we intend to expand the dataset to include a wider range of skin types in order to more comprehensively evaluate and improve the performance of our model.  
In the future, we aspire to evolve the model into a comprehensive system or a user-friendly mini programme that accommodates the input of suspected pathological images. The model will then analyse these images to determine the type and likelihood of skin diseases. This innovative system has the potential to greatly aid dermatologists in their auxiliary diagnostic processes and facilitate the remote diagnosis of skin diseases on  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & \
\begin{tabular}{c} **Data** \\ **Enhancement** \\ \end{tabular} &
\begin{tabular}{c} **Hair** \\ **Removal** \\ \end{tabular} & **Focal Loss** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline Exp.1 & \(\times\) & ✓ & ✓ & 0.8702 & 0.8422 & 0.8135 & 0.8304 \\ Exp.2 & ✓ & \(\times\) & ✓ & 0.8651 & 0.8331 & 0.8290 & 0.8315 \\ Exp.3 & ✓ & ✓ & \(\times\) & 0.8433 & 0.8209 & 0.8311 & 0.8260 \\ Exp.4 & ✓ & ✓ & ✓ & 0.8904 & 0.8737 & 0.9015 & 0.8876 \\ \hline \hline \end{tabulartelemedicine platforms. Furthermore, it will be necessary for more professionals to work together to transform the network model into a powerful tool in the hands of real clinicians.  
Conceptualization, S.Z., Z.L. and X.Y.; methodology, S.Z., Z.L. and X.Y.; data curation, S.Z.; formal analysis, S.Z.; writing--original draft preparation, S.Z.; writing--review and editing, Z.L., X.Y., K.S. and J.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.