ScalpEye: A Deep Learning-Based Scalp Hair Inspection and Diagnosis System for Scalp Health

Abstract

Many people suffer from scalp hair problems such as dandruff, folliculitis, hair loss, and oily hair due to poor daily habits, imbalanced nutritional intake, high stress, and toxic substances in their environment. To treat these scalp problems, dedicated services such as scalp hair physiotherapy have emerged in recent years. This article proposes a deep learning-based intelligent scalp inspection and diagnosis system, named ScalpEye, as an efficient inspection and diagnosis system for scalp hair physiotherapy as part of scalp healthcare. The proposed ScalpEye system consists of a portable scalp hair imaging microscope, a mobile device app, a cloud-based artificial intelligence (AI) training server, and a cloud-based management platform. The ScalpEye system can detect and diagnose four common scalp hair symptoms (dandruff, folliculitis, hair loss, and oily hair). In this study, we tested several popular object detection models and adopted a Faster R-CNN with the Inception ResNet_v2_Atrous model in the ScalpEye system for image recognition when inspecting and diagnosing scalp hair symptoms. The experimental results show that the ScalpEye system can diagnose four common scalp hair symptoms with an average precision (AP) ranging from 97.41% to 99.09%. INDEX TERMSArtificial intelligence over the Internet of Things (AIoT), deep learning, image processing, image recognition, inspection, haircare, healthcare, scalp hair diagnosis.

An Artificial Intelligence of Things-Based Picking Algorithm for Online Shop in the Society 5.0’s Context

“…In Figure 9b, block matching is applied to the disparity map. The disparities between the left image and the right image are derived from (11) and (12), where ε d R→L (x, y) is the normalized block matching error with the horizontal disparity d, W is window of the block matching, and D max is the maximum value disparity within the permissible limit. The following will obtain the disparity from the right image f r to the left image frame f l , to check the observed disparity, while u and v are the number of pixels in xy-camera image plane, respectively.…”

“…Some of the AIoT approaches work for other applications [ 10 ]. Two studies [ 11 , 12 ] have introduced AIoT for drug screening applications and hair health diagnostics. Including further research, MedGlasses helps visually impaired patients to take medication easily [ 13 ].…”

How good is artificial intelligence (AI) at solving hairy problems? A review of AI applications in hair restoration and hair disorders

“…[35][36][37] The prototype system (ScalpEye) was tested using different deep learning modules and achieved 97%-99% precision in detecting dandruff, folliculitis, hair loss, and oily hair. 21 A similar approach was used for a cosmetic application-hair tone estimation before hair coloring. To test the program, eight dermatologists assessed 400 images manually and with its assistance.…”

“…Since more hair and scalp treatments are being offered in nonspecialist settings, new AI‐based systems using an imaging device and an online deep learning database for image classification have been proposed for diagnosis of scalp conditions 35‐37 . The prototype system (ScalpEye) was tested using different deep learning modules and achieved 97%‐99% precision in detecting dandruff, folliculitis, hair loss, and oily hair 21 . A similar approach was used for a cosmetic application—hair tone estimation before hair coloring.…”

Efficient Hair Damage Detection Using SEM Images Based on Convolutional Neural Network

“…Chang. et al [19] proposed a smart scalp inspection and diagnosis system based on deep learning used to detect and diagnose four common scalp and hair symptoms (dandruff, folliculitis, hair loss, and oily hair). As a part of scalp health care, it is used for scalp and hair physiotherapy, and is an effective inspection and diagnosis system.…”