# Intelligent Healthcare Platform for Diagnosis of Scalp and Hair Disorders
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c} \hline
**Reference** & **Model** & **Description** & **Limitation** \\ \hline \multirow{2}{*}{[1]} & DenseNet with CBAM and & The severity of dandruff was examined & It can only assess one specific scalp and \\  & spinal FC & from a microscopic scalp image. & hair disorder without providing \\ \hline \multirow{2}{*}{[22]} & Triple ensemble model & The severity of alpecia was predicted & information about the lesion region. \\  & & from a microscopic scalp image. & \\ \hline \multirow{2}{*}{[4]} & \multirow{2}{*}{Faster R-CNN} & The ScalpEye system detected dandruff, & It cannot predict the severity of each \\  & & folliculitis, hair loss, and oily hair from & disorder and bounding box annotation \\  & & a microscopic scalp image. & is required. \\ \hline \multirow{2}{*}{[15]} & \multirow{2}{*}{ImageNet-VGG-f model} & Microscopic scalp images were & It can only judge whether the image is \\  & & classified into four disorders: bacteria & one of the four trained classes or not \\  & & type 1, bacteria type 2, allergy, & and cannot describe the lesion region. \\ \hline \multirow{2}{*}{[16]} & Ensemble of VGG-16 & Hair follicles on microscopic scalp & It cannot predict common scalp hair \\  & networks & images were counted. & disorders. \\ \hline \multirow{2}{*}{[17]} & \multirow{2}{*}{Three CNN-based models} & The severity level (1–4) of hair loss was & It can only inspect hair loss severity \\  & & predicted from facial images. & and the dataset does not contain \\ \hline \multirow{2}{*}{[18]} & \multirow{2}{*}{U-net} & The percentage of hair loss and severity & It can only inspect hair loss severity \\  & & of alpecia were calculated. & and pixel-level annotation is necessary. \\ \hline \multirow{2}{*}{[19]} & \multirow{2}{*}{VGG-SVM} & The model recognized alpecia from & It can only determine if the photo is \\  & & hair photos. & alpecia or healthy hair. \\ \hline \multirow{2}{*}{[10]} & \multirow{2}{*}{Mask R-CNN} & \multirow{2}{*}{Hair follicles were detected and hair} & It can only evaluate the hair loss \\  & & & severity from a microscopic scalp \\ \hline \multirow{2}{*}{[21]} & \multirow{2}{*}{EfficientDet, YOLOv4,  DetectoRS} & \multirow{2}{*}{density in each follicle was measured.} & It can only evaluate the hair loss \\  & & & severity from a microscopic scalp \\ \hline \end{tabular}
\end{table} 
Table 1: Summary of the existing deep learning models for scalp and hair healthcare. In the present study, we developed a deep learning-based intelligent platform to predict the severity of each scalp and hair disorder and localize the lesion region. The open dataset from AI-Hub was used to train deep learning models [23]. The dataset consists of six types of scalp hair disorders and four severity levels for each disorder. Pre-trained ResNet [24], EfficientNet [25], and ViT [26] models, which have been effective for the image classification task, were fine-tuned and the performance of the three models was evaluated. In addition, eXplainable Artificial Intelligence (XAI) techniques such as Grad-CAM [27] and attention rollout [28] methods were applied to highlight the disorder region in a scalp image depending on the type of scalp disorder. Finally, the user-friendly software was also developed to easily recognize hairy scalp health states and check trends of the severity of each disorder by date. In summary, the main contributions of this work are as follows: (1) we applied three deep learning models and assessed their performances to accurately estimate severities of six common scalp and hair disorders; (2) we utilized XAI methods to visually demonstrate model explainability and approximate lesion region; (3) we developed the assistant program 'Scalp checker' to help users analyze and track their hairy scalp health states. The remainder of this paper is organized as follows. Section 2 describes the dataset, deep learning models, and XAI visualization techniques used in this study.