# A Study on the Development of a Web Platform for Scalp Diagnosis Using EfficientNet
## 6 Conclusions  
The Scalp Diagnosis Web Platform represents a pioneering step in the integration of machine learning with practical health applications. By providing an accessible, efficient, and reliable method for scalp health assessment, with diagnostic accuracy rates of 75% for fine dandruff and 82% for perifollicular erythema, it has the potential to revolutionize personal health management. Here, the term \"diagnosis\" refers to the detection of the presence or absence of fine dandruff or erythema perifollicular. The analysis of user satisfaction indicates that this web application has achieved outstanding outcomes in terms of user satisfaction, garnering high evaluations for its usability, design effectiveness, and overall user experience with all the scores ranging between 4.4 and 4.8. The continued development and enhancement of this platform will contribute significantly to global scalp health, offering a robust tool for the early detection and treatment of scalp-related conditions.  
The potential future research directions based on this study include several key areas. First, expanding the dataset to encompass a more diverse range of populations and scalp conditions could enhance the generalizability of the diagnostic algorithms, thereby enabling more reliable diagnostics for a broader user base. Second, integrating advanced machine learning algorithms, such as deep learning or reinforcement learning, could improve the accuracy and efficiency of the diagnosis process. Third, the development of real-time diagnostic capabilities could significantly enhance user experience, allowing for immediate feedback and facilitating prompt intervention for early treatment. Fourth, expanding the diagnostic scope of the platform to include a wider array of scalp-related conditions beyond those currently addressed would provide a more comprehensive solution for scalp health management. Finally, leveraging user data to offer personalized scalp health management solutions could result in tailored recommendations, thereby contributing to more effective individual health management.  
Data collection and analysis, Y.-J.J., Y.-S.P., S.-H.K. and D.-H.K.; conceptualization, J.-XL. and D.-H.K.; methodology, S.-H.K. and D.-H.K.; software, Y.-J.J. and Y.-S.P.; validation, Y.-J.J., Y.-S.P., S.-H.K., D.-H.K. and J.-Y.L.; original draft preparation, Y.-J.J. and J.-Y.L.; writing--review and editing, [PERSON]-J.J. and J.-Y.L.; visualization, J.-Y.L.; funding acquisition, J.-Y.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement**: Ethical review and approval were waived for this study due to the usage of an open public dataset.  
**Informed Consent Statement**: Informed consent was obtained from all subjects involved in the study.  
Data Availability Statement: Publicly available datasets were analyzed in this study. This data can be found from AI-Hub: [[https://www.aihub.or.kr/aihubdata/data/view.do?currMenu=&topMenu=&aihubDataSe=data&dataSetSn=216](https://www.aihub.or.kr/aihubdata/data/view.do?currMenu=&topMenu=&aihubDataSe=data&dataSetSn=216)]([https://www.aihub.or.kr/aihubdata/data/view.do?currMenu=&topMenu=&aihubDataSe=data&dataSetSn=216](https://www.aihub.or.kr/aihubdata/data/view.do?currMenu=&topMenu=&aihubDataSe=data&dataSetSn=216)) (accessed on 21 August 2024).  
The authors declare no conflicts of interest.