# Intelligent Healthcare Platform for Diagnosis of Scalp and Hair Disorders
## 1 Introduction  
Scalp and hair disorders have become prevalent because of high stress, dietary changes, and exposure to harmful environmental substances. Common scalp and hair symptoms include dryness, oiliness, erythema, folliculitis, and dandruff [1, 2, 3, 4]. If these symptoms persist for a long time, they are commonly accompanied by inflammatory scalp disorders which may lead to severe alopecia [2, 5, 6]. Severe scalp and hair disorders can also affect one's appearance, resulting in losing self-confidence, generating additional social barriers, and degrading the quality of life [7, 8]. Therefore, it is crucial to diagnose various scalp and hair disorders at an early stage and receive proper care and treatment based on an accurate diagnostic result. In addition, the size of the global scalp and hair care market was valued at USD 91.60 billion in 2022 and is expected to grow annually by 5.8% with increasing demand for scalp and hair healthcare [9]. The current gold standard for diagnosing scalp hair conditions is a manual examination by a dermatologist or professional therapist. However, typical visual examination is time-consuming, inspector-dependent, and labor-intensive [1, 4, 10]. A patient should visit a dermatologist or professional hair therapist to find out the state of the scalp and hair. Diagnostic results may vary depending on the skill of the expert. Furthermore, long training time and high education costs are required to determine the scalp and hair condition. Deep learning has been widely introduced in various research fields including object recognition, natural language processing, bio-medical diagnosis, and treatment [11]. For example, [PERSON] et al. proposed convolutional neural network (CNN)-based models to simultaneously track multiple objects [12] and precisely detect multiple targets from infrared images [13]. A number of deep learning models have also been applied for scalp and hair healthcare (Table 1) [14]. [PERSON] et al. tested several machine learning algorithms and the ImageNet-VGG-f deep learning model to categorize microscopic scalp images into four groups: bacteria type 1, bacteria type 2, allergy, and dandruff images [15]. The same group proposed ScalpEye using a Faster R-CNN model to detect four scalp hair symptoms: dandruff, folliculitis, hair loss, and oily hair [4]. [PERSON] et al. adopted an ensemble of VGG-16 networks to mark hair follicles on microscopic images [16]. [PERSON] et al. used a CNN model with the convolutional block attention module (CBAM) and spinal fully connected (FC) layers to assess the severity of dandruff [1]. [PERSON] et al. compared the performance of three CNN-based models to detect hair loss severity from facial images [17]. [PERSON] et al. varied a conventional U-net model for identifying the scalp and hair loss area [18]. [PERSON] et al. suggested a VGG-SVM model to recognize alpecia [19]. [PERSON] et al. proposed D-Net and R-Net to detect hair follicles and evaluate hair density [20]. [PERSON] et al. detected hair follicles and predicted the number of hairs in each follicle using EfficientDet, YOLOv4, and DetoctoRS which are object detection networks [21]. [PERSON] et al. applied the Mask R-CNN framework to sort the hair follicle conditions into three groups (healthy, normal, and severe) [10]. [PERSON] et al. compared the performance of individual deep learning models and their ensemble models for diagnosing the severity of alpecia [22]. However, studies to simultaneously diagnose the severity and problem areas of various scalp and hair disorders have not yet been attempted.