# A Study on the Development of a Web Platform for Scalp Diagnosis Using EfficientNet
## 4 Results  
### Severity Classification of Two Hairy Scalp Disorders  
To measure the performance of this diagnostic model, we used the confusion matrix-based accuracy, recall, precision, F1-score, and support. Given that this model was for multi-class classification, accuracy was considered to be the most critical metric [39; 40]. Based on the results presented in Table 6, the accuracy of the fine dandruff diagnosis model was approximately 75%. This table highlights the type-specific learning results for the fine dandruff model, providing detailed performance metrics for different categories of dandruff severity: normal, mild, moderate, and severe. The model exhibited relatively high performance in identifying normal, mild, and severe cases, as indicated by their F1-scores, which were all above 70%. Specifically, the normal category achieved an F1-score of 0.84, reflecting the model's strong ability to correctly identify scalps in good condition. Similarly, the mild category had an F1-score of 0.71, and the severe category had an F1-score of 0.83, both indicating reliable performance in these severity levels. However, the model's performance in the moderate category was comparatively lower, with an F1-score of 0.68. This suggests that while the model can reasonably identify the moderate cases of dandruff, its performance is less accurate than in the other categories. The lower F1-score for the moderate category highlights an area where the model could be improved to achieve more balanced and accurate diagnostic capabilities across all the levels of dandruff severity. Overall, while the fine dandruff diagnosis model demonstrated robust performance in most categories, the moderate category's lower F1-score indicates a need for further refinement and additional training data to enhance its diagnostic precision. The comprehensive performance metrics provided in Table 6 offer valuable insights into the model's strengths and weaknesses, guiding future efforts to optimize and balance its diagnostic capabilities. Table 7 provides a comprehensive set of performance evaluation metrics for the perifollicular erythema diagnosis model. The metrics include precision, recall, F1-score, and support for different severity levels: normal, mild, moderate, and severe. Based on these results, the overall accuracy of the perifollicular erythema model was found to be approximately 82%. In more detail, the normal category achieved a precision of 0.84, a recall of 0.96, and an F1-score of 0.89, with a support value of 112, indicating the number of actual normal instances in the dataset. This high F1-score reflects the model's strong ability to correctly identify the normal conditions of perifollicular erythema. The mild category, on the other hand, showed comparatively lower performance metrics. It achieved a precision of 0.83, a recall of 0.73, and an F1-score of 0.78, with a support value of 278. Although the precision was fairly high, the recall and F1-scores were somewhat lower, indicating that the model has more difficulty accurately identifying the mild cases of perifollicular erythema. The model performed well in the moderate category, with a precision of 0.81, a recall of 0.84, and an F1-score of 0.82, with a support value of 278. These metrics demonstrate a balanced performance in identifying the moderate cases, with both precision and recall contributing to a strong F1-score. The severe category had the highest recall, at 0.97, but a slightly lower precision, at 0.75, resulting in an F1-score of 0.85 and a support value of 37. This indicates that while the model is very effective at identifying severe cases (high recall), it sometimes misclassifies other severity levels as severe (lower precision). \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Precision** & **Recall** & **F1-Score** & **Support** \\ \hline Normal & 0.73 & 0.98 & 0.84 & 107 \\ Mild & 0.84 & 0.62 & 0.71 & 300 \\ Moderate & 0.61 & 0.76 & 0.68 & 300 \\ Severe & 0.88 & 0.79 & 0.83 & 300 \\ \hline \hline \end{tabular}
\end{table}