# Intelligent Healthcare Platform for Diagnosis of Scalp and Hair Disorders
## 4 Discussion  
This paper presented an end-to-end intelligent healthcare platform that predicts the severity levels of six scalp disorders, highlights the regions of suspected lesions, and enables users to easily check their scalp conditions via the developed Scalp checker software. In this study, two CNN models (ResNet-152, EfficientNet-B6) and the transformer model (ViT-B/16) were applied to predict the severity level of each disorder. To the best of our knowledge, there are no studies that have applied the ViT model for diagnosing scalp and hair conditions. As a result, the ViT-B/16 model showed the highest accuracy, precision, recall, and F1-score among the tested deep learning models (Figure 3). ViT is a pure self-attention-based architecture without any convolution layer [26]. [PERSON] et al. reported that ViT models pre-trained on large datasets (ImageNet-21k or JFT-300M) showed excellent classification performance, compared to CNN models [26]. In addition, ViT models required considerably fewer computational resources for training, so ViT models generally showed better performance than CNN models with the same computational budget. As the ViT-B/16 model (86 M) can learn more parameters compared to ResNet-152 (58 M) and EfficientNet-B6 (41 M) under the same GPU capability and better focus on the image region semantically relevant for classification via multiheaded self-attention (Figure 5), the ViT-B/16 model outperformed the two tested CNN models. However, the trained ViT-B/16 model showed relatively low performance for severity prediction of oiliness (Figure 4b and Table 4). In particular, test images with severity level 2 of oiliness were incorrectly classified as severity level 1 or level 3 of oiliness (Figure 4b). The severity level of oiliness was classified based on the intensity of reflected light by excessive oil in a photograph [2]. Because the amount of reflected light is sensitively altered by the surrounding environment or other structures (e.g., dandruff), it can be the reason why classifying the severity level of oiliness is more difficult compared to the other five scalp hair disorders. The attention rollout XAI technique was adopted to visualize the lesion from the ViT-B/16 model trained by each scalp and hair disorder (Figure 5e-g). The locations of the lesion were more accurately detected by applying other CNN models for object detection, such as Faster R-CNN [4], Mask R-CNN [10], U-net [18], and YOLO [21] models. However, extensive pixel-level [18] or bounding box annotations [4; 10; 21] were essentially required. The attention map can be directly obtained from the trained ViT-B/16 model and attention \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Operating System** & **CPU** & **Architecture** & **RAM** & **VRAM** & **Elapsed Time** & **Others** \\ \hline Windows XP (SP3) & 4 cores & \(\times\)86 & 4 GB & 4 GB & N/A &.NET Framework 4.8 \\ Windows Vista (SP2) & 4 cores & \(\times\)64 & 8 GB & 4 GB & N/A & is not supported \\ Windows 7 (SP1) & 4 cores & \(\times\)64 & 8 GB & 4 GB & N/A & Python 3.9 is not \\ Windows 8.1 (Update 1) & 4 cores & \(\times\)64 & 4 GB & N/A & \(\coloneqq\)229 s & supported \\ Windows 8.1 (Update 1) & 4 cores & \(\times\)64 & 8 GB & N/A & \(\coloneqq\)213 s & \\ Windows 10 (22H2) & 4 cores & \(\times\)64 & 4 GB & N/A & \(\coloneqq\)227 s & \\ Windows 10 (22H2) & 20 cores & \(\times\)64 & 32 GB & 12 GB & \(\coloneqq\)40 s & \\ Windows 11 (22H2) & 20 cores & \(\times\)64 & 32 GB & N/A & \(\coloneqq\)60 s & \\ Windows 11 (22H2) & 20 cores & \(\times\)64 & 32 GB & 12 GB & \(\coloneqq\)40 s & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The operation of Scalp checker software under diverse environments. rollout without any annotation or re-training processes. Furthermore, it provides not only model interpretability but also approximate lesion areas as the class-discriminative localization map (Figure 5e-g). Several studies about the severity prediction of scalp hair disorders have been introduced (Table 5).