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...
Autores principales: | , , , , , , , , , , , , , |
---|---|
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 |