Cargando…

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

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 syst...

Descripción completa

Detalles Bibliográficos
Autores principales: Huynh, Quan Thanh, Nguyen, Phuc Hoang, Le, Hieu Xuan, Ngo, Lua Thi, Trinh, Nhu-Thuy, Tran, Mai Thi-Thanh, Nguyen, Hoan Tam, Vu, Nga Thi, Nguyen, Anh Tam, Suda, Kazuma, Tsuji, Kazuhiro, Ishii, Tsuyoshi, Ngo, Trung Xuan, Ngo, Hoan Thanh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9406819/
https://www.ncbi.nlm.nih.gov/pubmed/36010229
http://dx.doi.org/10.3390/diagnostics12081879
_version_ 1784774214398509056
author Huynh, Quan Thanh
Nguyen, Phuc Hoang
Le, Hieu Xuan
Ngo, Lua Thi
Trinh, Nhu-Thuy
Tran, Mai Thi-Thanh
Nguyen, Hoan Tam
Vu, Nga Thi
Nguyen, Anh Tam
Suda, Kazuma
Tsuji, Kazuhiro
Ishii, Tsuyoshi
Ngo, Trung Xuan
Ngo, Hoan Thanh
author_facet Huynh, Quan Thanh
Nguyen, Phuc Hoang
Le, Hieu Xuan
Ngo, Lua Thi
Trinh, Nhu-Thuy
Tran, Mai Thi-Thanh
Nguyen, Hoan Tam
Vu, Nga Thi
Nguyen, Anh Tam
Suda, Kazuma
Tsuji, Kazuhiro
Ishii, Tsuyoshi
Ngo, Trung Xuan
Ngo, Hoan Thanh
author_sort Huynh, Quan Thanh
collection PubMed
description 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 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 1572 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 to help acne patients better understand their conditions and support doctors in acne diagnosis.
format Online
Article
Text
id pubmed-9406819
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-94068192022-08-26 Automatic Acne Object Detection and Acne Severity Grading Using Smartphone Images and Artificial Intelligence Huynh, Quan Thanh Nguyen, Phuc Hoang Le, Hieu Xuan Ngo, Lua Thi Trinh, Nhu-Thuy Tran, Mai Thi-Thanh Nguyen, Hoan Tam Vu, Nga Thi Nguyen, Anh Tam Suda, Kazuma Tsuji, Kazuhiro Ishii, Tsuyoshi Ngo, Trung Xuan Ngo, Hoan Thanh Diagnostics (Basel) Article 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 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 1572 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 to help acne patients better understand their conditions and support doctors in acne diagnosis. MDPI 2022-08-03 /pmc/articles/PMC9406819/ /pubmed/36010229 http://dx.doi.org/10.3390/diagnostics12081879 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Huynh, Quan Thanh
Nguyen, Phuc Hoang
Le, Hieu Xuan
Ngo, Lua Thi
Trinh, Nhu-Thuy
Tran, Mai Thi-Thanh
Nguyen, Hoan Tam
Vu, Nga Thi
Nguyen, Anh Tam
Suda, Kazuma
Tsuji, Kazuhiro
Ishii, Tsuyoshi
Ngo, Trung Xuan
Ngo, Hoan Thanh
Automatic Acne Object Detection and Acne Severity Grading Using Smartphone Images and Artificial Intelligence
title Automatic Acne Object Detection and Acne Severity Grading Using Smartphone Images and Artificial Intelligence
title_full Automatic Acne Object Detection and Acne Severity Grading Using Smartphone Images and Artificial Intelligence
title_fullStr Automatic Acne Object Detection and Acne Severity Grading Using Smartphone Images and Artificial Intelligence
title_full_unstemmed Automatic Acne Object Detection and Acne Severity Grading Using Smartphone Images and Artificial Intelligence
title_short Automatic Acne Object Detection and Acne Severity Grading Using Smartphone Images and Artificial Intelligence
title_sort automatic acne object detection and acne severity grading using smartphone images and artificial intelligence
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9406819/
https://www.ncbi.nlm.nih.gov/pubmed/36010229
http://dx.doi.org/10.3390/diagnostics12081879
work_keys_str_mv AT huynhquanthanh automaticacneobjectdetectionandacneseveritygradingusingsmartphoneimagesandartificialintelligence
AT nguyenphuchoang automaticacneobjectdetectionandacneseveritygradingusingsmartphoneimagesandartificialintelligence
AT lehieuxuan automaticacneobjectdetectionandacneseveritygradingusingsmartphoneimagesandartificialintelligence
AT ngoluathi automaticacneobjectdetectionandacneseveritygradingusingsmartphoneimagesandartificialintelligence
AT trinhnhuthuy automaticacneobjectdetectionandacneseveritygradingusingsmartphoneimagesandartificialintelligence
AT tranmaithithanh automaticacneobjectdetectionandacneseveritygradingusingsmartphoneimagesandartificialintelligence
AT nguyenhoantam automaticacneobjectdetectionandacneseveritygradingusingsmartphoneimagesandartificialintelligence
AT vungathi automaticacneobjectdetectionandacneseveritygradingusingsmartphoneimagesandartificialintelligence
AT nguyenanhtam automaticacneobjectdetectionandacneseveritygradingusingsmartphoneimagesandartificialintelligence
AT sudakazuma automaticacneobjectdetectionandacneseveritygradingusingsmartphoneimagesandartificialintelligence
AT tsujikazuhiro automaticacneobjectdetectionandacneseveritygradingusingsmartphoneimagesandartificialintelligence
AT ishiitsuyoshi automaticacneobjectdetectionandacneseveritygradingusingsmartphoneimagesandartificialintelligence
AT ngotrungxuan automaticacneobjectdetectionandacneseveritygradingusingsmartphoneimagesandartificialintelligence
AT ngohoanthanh automaticacneobjectdetectionandacneseveritygradingusingsmartphoneimagesandartificialintelligence