# A Study on Multiple Factors Affecting the Accuracy of Multiclass Skin Disease Classification
## 4 Experimental Results and Discussion  
In order to evaluate the effects of different factors on the classification accuracy of skin diseases, experiments were carried out on the custom four-class HAM10000 dataset. The four-fold cross-validation setup was used for all experiments to ensure less bias on the estimation of models' performance. Each fold contained 1500 images in the training set and 500 images in the validation set. The choice of the base architecture was first evaluated. The AlexNet-based Model I was tested. The images were resized to 227 \(\times\) 227 pixels to fit the input size of the AlexNet input layer. The transferred layers were frozen by assigning small learning rates to ensure only the newly added FC layer was trained and the features learned from the ImageNet database could be appropriately transferred. For each fold of cross-validation, the Adam solver was used to train the network for 10 epochs. A mini-batch size of 10 was used. No data augmentation was applied during training. The obtained average cross-validation accuracy was 0.7100, as listed in Table 1.  
An experiment was carried out on Model I again with the same experimental setup as above, except that all the weights in the transferred layers were removed, and the model was trained from scratch. The average cross-validation accuracy obtained was 0.6640. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Architecture** & **Training** & **Data Augmentation** & **Accuracy** \\ \hline I & AlexNet-based & Transfer learning & No & 0.7100 \\ I & AlexNet-based & From scratch & No & 0.6640 \\ II & Four-stage ResNet50-based & Transfer learning & No & 0.7640 \\ II & Four-stage ResNet50-based & From scratch & No & 0.6605 \\ III & Three-stage ResNet50-based & Transfer learning & No & 0.7700 \\ III & Three-stage ResNet50-based & From scratch & No & 0.6415 \\ IV & Two-stage ResNet50-based & Transfer learning & No & 0.7835 \\ IV & Two-stage ResNet50-based & From scratch & No & 0.6960 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Average cross-validation results of different models. The ResNet50-based Model II was tested. The four-stage Model II was trained using the Adam solver for 10 epochs for each fold, with a mini-batch size of 10. Transfer learning was used to transfer the weights of ResNet50 pretrained on the ImageNet database. No data augmentation techniques were used. The average classification accuracy for four folds was 0.7640. The Model II architecture with all the pretrained weights removed was trained from scratch and showed an average accuracy of 0.6605. Because the ResNet50-based Model II clearly has higher accuracy than the AlexNet-based Model I when using transfer learning, the former was chosen to be further studied for network depth reduction and potential accuracy improvement. The ResNet50-based models, III and IV, with different network depths were then evaluated with the same four-fold cross-validation setup. Similarly, transfer learning was applied to Model III and Model IV, and the average cross-validation accuracy values for the two models were 0.7700 and 0.7835, respectively. The above results are summarized in Table 1. It can be found that in the context of transfer learning, the choice of architecture has a huge impact on the classification accuracy of skin diseases, The ResNet50-based models outperform the AlexNet-based model by at least 0.05, which shows the supremacy of ResNet50 over AlexNet in skin disease classification. As for transfer learning versus training from scratch, no matter which architecture is used, whether the AlexNet-based model or the ResNet50-based model, transfer learning is evidently better than training from scratch, as compared in Table 1. As for the ResNet-50-based models with different depths, using the transferred weights learned from the large ImageNet database improves the accuracy by at least 0.08 compared with those trained from scratch.