# A Study on the Development of a Web Platform for Scalp Diagnosis Using EfficientNet
## 2 Research Backgrounds  
### Related Works  
Various studies on diagnosing scalp conditions have utilized image processing and deep learning technologies. [PERSON] et al. (2018) used a microscopy image analysis technique, which quantitatively measures four scalp characteristics for use in scalp diagnoses: hair density, hair thickness, the number of pores, and erythema. The image processing techniques include contrast stretching, morphological operations, and edge detection to accurately identify hair and scalp features. The experimental results demonstrated the system's accuracy and reliability, providing a practical tool for scalp self-diagnosis and management. [PERSON] et al. (2019) proposed a method for assessing scalp conditions using the impedance method. This approach is useful for distinguishing between dry, oily, and normal scalp conditions and can be applied in a non-invasive and convenient manner. The research findings indicated that changes in impedance values have a significant correlation with scalp conditions, demonstrating that this method can be an effective tool for diagnosing and managing scalp health. [PERSON] et al. (2018) suggested a scalp diagnosis system comprising an Android-based device and server. Initially, scalp images are taken via a dermoscopic camera, then hair thickness, density, and the presence of scalp inflammation are assessed using image processing, and deep learning is then used to analyze the number and distribution of hair strands. The system also explored the possibility of allowing users to share and consult on scalp conditions with experts in real time via video calling using Web real-time communication (WebRTC), enabling self-diagnosis with an affordable endoscopic camera and smart device, and providing a comprehensive analysis of the hair and scalp. [PERSON] et al. (2014) developed a mobile scalp diagnosis system which uses a smartphone and macro lens to capture images of the scalp, diagnosing scalp conditions using fuzzy theory and data mining techniques. The research results demonstrated the system's accuracy and usefulness, showing its potential to contribute to the smart healthcare field. [PERSON] et al. (2015) proposed an intelligent scalp examination and diagnosis system called \"ScalpEye\" using Faster Regions with Convolutional Neural Networks (R-CNN) for deep learning-based detection. The ScalpEye system consists of a portable microscope, a mobile device app, a cloud-based AI training server, and a cloud-based management platform. It is capable of detecting and diagnosing four common scalp conditions: dandruff, folliculitis, hair loss, and oily hair. The experimental results showed that the ScalpEye system operated with high precision, ranging from 97.41% to 99.09%. [PERSON] et al. (2016) developed a mobile web service for the early diagnosis and prevention of scalp diseases by applying CNN based on EfficientNet-B2 techniques. The system operates through data communication between the web, AI server, and database. The web application is developed based on React, with the user interface structured using the model, view, and controller (MVC) pattern powered by \"Spring Boot\". When a user uploads an image, it is sent to a Flask server for analysis. This system is also available in a mobile environment, allowing users to take scalp photos using their smartphone cameras and upload them. The server analyzes the images in real time and returns the results in java-script object notation (JSON) format, displaying the diagnostic outcomes to the user. [PERSON] et al. (2018) proposed a method to visually show the presence and location of keratin for the early diagnosis of seborrheic dermatitis. The Faster R-CNN model with Atrous convolution can detect keratin with high accuracy, suggesting the potential for the easy and precise self-diagnosis of scalp health conditions. [PERSON] et al. (2017) developed the EfficientNet-B0 model for diagnosing scalp conditions using AI Hub's \"type-specific scalp image\" data. They implemented it as a smartphone application.