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

January 2020 in “ IEEE Access ”

Wan‐Jung Chang, Liang-Bi Chen, Ming-Che Chen, Yi-Chan Chiu, Jian-Yu Lin

scalp hair inspection dandruff folliculitis hair loss oily hair deep learning Faster R-CNN Inception ResNet_v2_Atrous image recognition scalp healthcare

TLDR ScalpEye accurately diagnoses scalp issues like dandruff and hair loss.

The study introduced ScalpEye, a deep learning-based system designed for inspecting and diagnosing scalp hair issues such as dandruff, folliculitis, hair loss, and oily hair. The system included a portable imaging microscope, a mobile app, and a cloud-based AI server and management platform. ScalpEye utilized the Faster R-CNN with the Inception ResNet_v2_Atrous model for image recognition, achieving an average precision between 97.41% and 99.09% in diagnosing the four common scalp symptoms. This system aimed to enhance scalp healthcare by providing efficient and accurate diagnoses.