# A Study on the Development of a Web Platform for Scalp Diagnosis Using EfficientNet
## 5 Discussion
Traditional diagnostic methods often involve time-consuming visits to specialists, relying heavily on subjective visual assessments [6; 7; 42]. These methods can be inconsistent and inconvenient for users [6; 7]. The web platform developed in this study allows users to upload images of their scalp and receive instant diagnostic feedback. This immediate response capability is a considerable improvement on traditional methods, offering users a convenient and efficient way to monitor and manage their scalp health. The platform's ability to provide diagnostic results, similar case comparisons, and suggested solutions further adds to its value as it promotes proactive health management. Overall, this web application has achieved excellent results in terms of user satisfaction, receiving high ratings for ease of use, design effectiveness, and overall user experience. Although there is potential for enhancement in the areas of functionality comprehension, response speed, and error resolution, the overall user satisfaction with the application remains high. Despite the promising results, this study had several limitations that warrant discussion. One significant limitation was the reduced dataset used for the model training, which was constrained by the development environment. The small dataset most likely impacted the model's overall accuracy, particularly in diagnosing the moderate cases of fine dandruff. In this study, the small dataset resulted in a lack of diversity, which, in turn, led to inadequate model training. Consequently, the model was unable to effectively learn the necessary patterns. This limitation highlights the importance of a comprehensive and balanced dataset to train machine learning models capable of handling nuanced variations in medical conditions. Another limitation was the moderate category's lower performance in fine dandruff diagnosis. Moderate conditions are often more challenging to diagnose accurately due to their subtle and varied presentations. This issue underscores the necessity for more granular and detailed data to improve the model's ability to differentiate between the severity levels of scalp conditions. To build upon the current study's success, future research should focus on several key areas: Expanding the dataset to include a more diverse range of scalp conditions and ensuring balanced class distributions would significantly enhance the model's robustness and accuracy. Accordingly, we will integrate existing public datasets related to scalp conditions to enhance data diversity. Furthermore, we plan to collaborate with various clinics, hospitals, and research institutions to gather a more comprehensive dataset. Employing advanced data augmentation techniques could also help mitigate data scarcity issues and improve model generalization. For example, employing synthetic data generation techniques, such as generative adversarial networks (GANs), can produce realistic images of scalp conditions. This method can potentially address the limitations associated with a small dataset. Exploring other deep learning architectures and hybrid models could also provide further improvements in diagnostic performance. Additionally, integrating more personalized treatment recommendations based on users' specific scalp conditions and medical history could enhance the platform's utility and user satisfaction. Further studies should also consider the incorporation of user feedback to continually refine and improve the platform. Moreover, real-world application and user interaction would provide valuable insights into usability and effectiveness, guiding iterative improvements.