# A Study on Multiple Factors Affecting the Accuracy of Multiclass Skin Disease Classification
## 5 Conclusions  
In this paper, potential approaches to improve the classification accuracy of skin diseases are investigated based on deep learning. Transfer learning using off-the-shelf  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Architecture** & **Training** & **Data Augmentation** & **Accuracy** \\ \hline IV & Two-stage ResNet50-based & Transfer learning & No & 0.7835 \\ IV & Two-stage ResNet50-based & Transfer learning & Random scale & 0.7785 \\ IV & Two-stage ResNet50-based & Transfer learning & Random rotation & 0.7620 \\ IV & Two-stage ResNet50-based & Transfer learning & Random reflection & 0.7735 \\ IV & Two-stage ResNet50-based & Transfer learning & Random shear & 0.7765 \\ IV & Two-stage ResNet50-based & Transfer learning & Combined & 0.7430 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of types of augmentation techniques.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Architecture** & **Training** & **Mini-Batch Size** & **Accuracy** \\ \hline II & Four-stage ResNet50-based & Transfer learning & 5 & 0.7560 \\ II & Four-stage ResNet50-based & Transfer learning & 10 & 0.7640 \\ II & Four-stage ResNet50-based & Transfer learning & 20 & 0.7765 \\ II & Four-stage ResNet50-based & Transfer learning & 40 & 0.7905 \\ II & Four-stage ResNet50-based & Transfer learning & 80 & 0.7855 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of different mini-batch sizes.  
Figure 7: (a) Confusion matrix of Model II with a mini-batch size of 5; (b) confusion matrix of Model II with a mini-batch size of 40.  
deep networks is common practice for image classification. However, the use of complete off-the-shelf networks feels too complex and is not necessary. Multiple factors are studied regarding the classification accuracy of skin diseases. Experiments were carried out using the HAM10000 skin disease dataset. The results show that the choice of the network architecture has a huge impact on accuracy. The ResNet50-based model largely outperforms the AlexNet-based model. The model trained from scratch is much less accurate than that using transfer learning and pretrained on the massive ImageNet database. In order to reduce the complexity of the models, the depths of the networks were reduced, and the two-stage model shows the best accuracy compared with the three-stage model and the four-stage model, which suggests reducing the network depths does not necessarily sacrifice accuracy in skin disease classification. Additionally, the use of different data augmentation techniques actually lowers the accuracy compared with no augmentation applied at all and should be used with caution. Furthermore, carefully choosing the mini-batch size can also help in improving accuracy.  
Conceptualization, Y.L.; methodology, J.F. and Y.L.; investigation, J.F.; writing--original draft preparation, J.F.; supervision, J.K., I.J. and Y.L. All authors have read and agreed to the published version of the manuscript.  
This study was supported by internal project funds by the Seoul National University of Science and Technology and by a grant from the Korea Institute of Radiological and Medical Sciences (KIRAMS), funded by the Ministry of Science and ICT (MSIT), Republic of Korea (No. 50538-2021).  
Not applicable.  
Restrictions apply to the availability of these data. Data was obtained from [[https://doi.org/10.7910/DVN/DBW86T](https://doi.org/10.7910/DVN/DBW86T)]([https://doi.org/10.7910/DVN/DBW86T](https://doi.org/10.7910/DVN/DBW86T)).  
We acknowledge the support of the Seoul National University of Science and Technology and the Korea Institute of Radiological and Medical Sciences (KIRAMS).  
The authors declare no conflict of interest.