# A Study on the Development of a Web Platform for Scalp Diagnosis Using EfficientNet
## 5 Discussion  
This study represents a noteworthy advancement in the field of scalp health diagnosis, presenting both a robust machine learning model and a practical application through a web-based platform. The utilization of EfficientNet-B0 in diagnosing scalp conditions such as fine dandruff and perifollicular erythema demonstrates promising results, with the model achieving accuracy rates of 75% and 82%, respectively. When comparing the results of this study with the most recent study [22], it is observed that [22] achieved an accuracy of 77.5% in diagnosing fine dandruff, while this study achieved an accuracy of 75%. Although there is a difference of approximately 2.5%, the performance levels are generally similar. This \begin{table}
\begin{tabular}{l l r} \hline \hline  & **Questionnaire** & **Average Scores** \\ \hline
1. & Using this web application is simple and intuitive. & 4.5 \\
2. & I found the design of the web application to be user-friendly. & 4.7 \\
3. & It did not require much effort to understand the functions of & 4.4 \\
4. & I did not feel confused while using the web application. & 4.6 \\
5. & This web application helps me easily accomplish the tasks I want & 4.8 \\
6. & The response speed of the web application is fast and smooth. & 4.5 \\
7. & When errors occurred during use, I could easily resolve them. & 4.6 \\
8. & I am willing to use this web application again. & 4.7 \\
9. & I find using it on a computer convenient and accessible. & 4.6 \\
10. & I would recommend this web application to others. & 4.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Questionnaire and analysis results for Scalp Diagnosis Web Platform. discrepancy may be attributed to the differences in the dataset size, preprocessing methods, or specific aspects of the training and validation processes. In diagnosing perifollicular erythema, [22] reported an accuracy of 84.8%, whereas this study recorded an accuracy of 82%. Despite a 2.8% difference, both studies demonstrate comparable performance. In conclusion, the results of this study and those of [22] exhibit a similar level of achievement overall. The minor differences are likely due to the variations in the dataset size or specific settings during model training. This consistency in the results between the two studies, both utilizing the AI-Hub Scalp Images database and EfficientNet-B0 model, highlights the reliability of the findings. These results underscore the potential of machine learning in medical diagnostics, particularly in providing accessible and reliable health assessments. One of the significant strengths of this research study was the integration of EfficientNet-B0. EfficientNet-B0 is renowned for its efficient scaling of CNNs, balancing the trade-offs between model depth, width, and resolution [22; 24; 25]. This balance allows for high performance with relatively low computational requirements, making it an excellent choice for applications requiring real-time processing and deployment on devices with limited resources [7; 22; 23; 24]. By leveraging this architecture, the study addresses a critical need for efficient and scalable solutions in medical imaging. The implementation of transfer learning is another crucial aspect of this study. Transfer learning, which involves retraining a pre-existing model on a new, often smaller, dataset, proved to be effective in this study [6; 7; 32; 33]. The pre-trained EfficientNet-B0 model, which was initially trained on a large dataset, was fine-tuned using specific scalp condition images. This method is particularly advantageous when dealing with limited data as it reduces the need for extensive data collection and annotation. The study effectively demonstrated that transfer learning can enhance model performance and accuracy, even with a relatively small amount of domain-specific data [34; 37]. The developed web platform can significantly enhance the accessibility of scalp health diagnostics.