# A Study on the Development of a Web Platform for Scalp Diagnosis Using EfficientNet
###### Abstract  
Along with their physical health, modern people also need to manage the health of their scalp and hair due to changes in lifestyle habits, job stress, and environmental pollution. In this study, a machine learning model was developed to diagnose scalp conditions such as fine dandruff and perifollicular erythema. Then, transfer learning was conducted using EfficientNet-B0. A web platform that allows users to easily diagnose the condition of their scalp was also proposed. The results showed that the accuracy of the diagnosis model for fine dandruff and perifollicular erythema was 75% and 82%, respectively. It showed good performance in classifying normal, mild, moderate, and severe cases compared to previous studies. Finally, a fast and convenient web platform was developed where users can upload an image and immediately visualize their scalp condition, receive diagnostic results, and see similar cases and solutions. The analysis of user satisfaction indicates that this web application has achieved exceptional outcomes in terms of user satisfaction, garnering high evaluations for its usability, design effectiveness, and overall user experience. This setup enables users to easily check their scalp condition and is accessible to everyone, which is a significant advantage. This is expected to play a crucial role in contributing to global scalp health by advocating the benefits of the early detection and treatment of scalp-related conditions. machine learning; EfficientNet; scalp diagnosis; fine dandruff; perifollicular erythema; web platform +
Footnote †: journal: Medical Convergence  
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Traditional scalp diagnostic methods have relied mainly on patient visits to the doctor or specialist for a consultation depending on subjective opinions and memories, limiting accuracy. Additionally, many products and services that are promoted as improving scalp health disseminate inaccurate or unreliable information through misleading advertising and information [6; 7]. Although there has been previous research on providing scalp diagnosis services using deep learning models such as EfficientNet-B0, VGGNet based on support vector machine (VGG-SVM), convolutional neural network (CNN), and deep neural network (DNN) [7; 8; 9; 10; 11; 12; 13], the development of websites and platforms has been lacking. The platform introduced a convenient scalp diagnosis system utilizing a smartphone and a portable scalp microscope [6; 7; 14], integrating artificial intelligence over the Internet of Things (AIoT) to enable the real-time management of diagnosis results and seamless integration with various services [15]. Nevertheless, the system's validation approach predominantly emphasizes deep learning accuracy [6; 7; 14; 15], underscoring the necessity for a more comprehensive evaluation that includes factors such as user satisfaction and ease of use. Moreover, the diagnostic accuracy of previous models, ranging from 65% to 77%, has not been significantly meaningful and, while diagnostic results through the classification of alopecia have been made available, the provision of solutions has often been insufficient [6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17]. That is, the system provides diagnostic results but does not offer personalized solutions tailored to the specific conditions of the user's scalp. Therefore, in this study, we aimed to overcome the limitations of the existing scalp diagnostic systems by developing a fast and accurate web platform service which can provide personalized solutions and diagnose conditions such as fine dandruff and perifollicular erythema using the EfficientNet model. Here, the term \"diagnosis\" refers to the detection of the presence or absence of fine dandruff or erythema perifollicular. The web platform will allow users to easily take photos of their scalp and receive online diagnoses at any time. This will enable users to immediately check their scalp health and take necessary actions quickly.