Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Automatic Acne Object Detection and Acne Severity Grading Using Smartphone Images and Artificial Intelligence

Quan Thanh Huynh
,
Phuc Hoang Nguyen
ORCID logo ,
Hieu Xuan Le
,
Lua Thi Ngo
,
Nhu-Thuy Trinh
,
Mai Thi-Thanh Tran
,
Hoan Tam Nguyen
,
Nga Thi Vu
,
Anh Tam Nguyen
,
Kazuma Suda
,
Kazuhiro Tsuji
,
Tsuyoshi Ishii
,
Trung Xuan Ngo
* ,
Hoan Thanh Ngo
* ORCID logo
Version 1 : Received: 28 June 2022 / Approved: 28 June 2022 / Online: 28 June 2022 (10:05:25 CEST)

A peer-reviewed article of this Preprint also exists.

Huynh, Q.T.; Nguyen, P.H.; Le, H.X.; Ngo, L.T.; Trinh, N.-T.; Tran, M.T.-T.; Nguyen, H.T.; Vu, N.T.; Nguyen, A.T.; Suda, K.; Tsuji, K.; Ishii, T.; Ngo, T.X.; Ngo, H.T. Automatic Acne Object Detection and Acne Severity Grading Using Smartphone Images and Artificial Intelligence. Diagnostics 2022, 12, 1879. Huynh, Q.T.; Nguyen, P.H.; Le, H.X.; Ngo, L.T.; Trinh, N.-T.; Tran, M.T.-T.; Nguyen, H.T.; Vu, N.T.; Nguyen, A.T.; Suda, K.; Tsuji, K.; Ishii, T.; Ngo, T.X.; Ngo, H.T. Automatic Acne Object Detection and Acne Severity Grading Using Smartphone Images and Artificial Intelligence. Diagnostics 2022, 12, 1879.

Abstract

Skin image analysis using artificial intelligence (AI) has recently attracted significant research interest, particularly for analyzing skin images captured by mobile devices. Acne is one of the most common skin conditions with profound effects in severe cases. In this study, we developed an AI system called AcneDet for automatic acne object detection and acne severity grading using facial images captured by smartphones. AcneDet includes two models for conducting two tasks: (1) a Faster R-CNN-based deep learning model for the detection of acne lesion objects of four types including blackheads/whiteheads, papules/pustules, nodules/cysts, and acne scars; and (2) a LightGBM machine learning model for grading acne severity using the Investigator’s Global Assessment (IGA) scale. The output of the Faster R-CNN model, i.e., the counts of each acne type, were used as input for the LightGBM model for acne severity grading. A dataset consisting of 1,572 labeled facial images captured by both iOS and Android smartphones was used for training. The results show that the Faster R-CNN model achieves a mAP of 0.54 for acne object detection. The mean accuracy of acne severity grading by the LightGBM model is 0.85. With this study, we hope to contribute to the development of artificial intelligent systems that are able to help acne patients understand more about their conditions and support doctors in acne diagnosis.

Keywords

Deep Learning; Smartphone Image; Acne Grading; Acne Object DetectionDeep Learning, Smartphone Image, Acne Grading, Acne Object Detection

Subject

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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