# A Study on the Development of a Web Platform for Scalp Diagnosis Using EfficientNet
## 4 Results
### Scalp Diagnosis Web Platform  
The development of the Scalp Diagnosis Web Platform marks a significant innovation in the field of scalp health management. This platform utilizes the EfficientNet-B0 model to diagnose scalp conditions such as fine dandruff and perifollicular erythema, achieving diagnostic accuracy rates of 75% and 82%, respectively. The primary objective of this platform is to provide users with an accessible, efficient, and reliable method to assess and manage their scalp health in real time. The Scalp Diagnosis Web Platform allows users to upload images of their scalp directly through a web interface. Once the image is uploaded, the platform processes it using the trained EfficientNet-B0 model. This involves converting the image into a suitable format, normalizing it, and then passing it through the neural network to obtain a diagnostic result. The platform is designed to be user-friendly, ensuring that even users with minimal technical knowledge can easily navigate and utilize the service. Figure 6 illustrates the overall flow of the platform. When a user uploads an image, it is sent to the server and processed through PIL. The processed image is then input into the EfficientNet-B0 model to generate diagnostic results. These results are returned to the user, visualized, and accompanied by similar cases and solutions. The back-end of the platform was built \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Precision** & **Recall** & **F1-Score** & **Support** \\ \hline Normal & 0.84 & 0.96 & 0.89 & 59 \\ Mild & 0.83 & 0.73 & 0.78 & 278 \\ Moderate & 0.81 & 0.84 & 0.82 & 278 \\ Severe & 0.75 & 0.97 & 0.85 & 37 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance evaluation metrics for the perifollicular erythema diagnosis model. using Python ver.3.11, which handles image processing and model inference. The images uploaded by users are converted into PIL Image objects, transformed into arrays, and normalized to match the input requirements of the EfficientNet-B0 model. The model, which was trained and stored in an H5 format, is then used to generate diagnostic results. This setup allows for swift and accurate diagnostics, providing users with immediate feedback on their scalp condition. The front-end was developed using JavaScript, with the Chart.js library employed to visualize diagnostic results. The users receive a graphical representation of their scalp condition, categorized into stages (normal, mild, moderate, and severe), and they are also provided with suggested solutions and similar case comparisons. This visual and informative feedback is crucial in enabling users to understand their scalp health status and take appropriate actions. Solutions are features that inform users about appropriate treatment and prevention methods based on their scalp condition. For mild symptoms, the solution content includes internal health management--such as diet, care habits, and nutritional management--as well as external care--such as using shampoos and essences that are suitable for the individual's scalp type and massages. For moderate to severe symptoms, it includes the aforementioned advice with an added recommendation to consult a doctor as a priority [41]. Similar cases have used the existing type-specific scalp image data from AI-Hub, providing two images each that match the respective conditions. Figures 7 and 8 depict the final GUI that is presented to the users. Figure 7 shows the interface for uploading images. Users can easily upload images through this interface, and the uploaded images are processed in real time, with the diagnostic results visualized for the user. Figure 8 depicts the results screen of the scalp diagnosis system. Similar cases for the scalp's microscaling condition can be viewed in the \"Microscaling step1 Similar Cases\" section, which includes two images showing the side of the head and the top of the head.