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Identification of Skin Lesions by Using Single-Step Multiframe Detector
An artificial intelligence algorithm to detect mycosis fungoides (MF), psoriasis (PSO), and atopic dermatitis (AD) is demonstrated. Results showed that 10 s was consumed by the single shot multibox detector (SSD) model to analyze 292 test images, among which 273 images were correctly detected. Verif...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7796252/ https://www.ncbi.nlm.nih.gov/pubmed/33406761 http://dx.doi.org/10.3390/jcm10010144 |
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author | Hsiao, Yu-Ping Chiu, Chih-Wei Lu, Chih-Wei Nguyen, Hong Thai Tseng, Yu Sheng Hsieh, Shang-Chin Wang, Hsiang-Chen |
author_facet | Hsiao, Yu-Ping Chiu, Chih-Wei Lu, Chih-Wei Nguyen, Hong Thai Tseng, Yu Sheng Hsieh, Shang-Chin Wang, Hsiang-Chen |
author_sort | Hsiao, Yu-Ping |
collection | PubMed |
description | An artificial intelligence algorithm to detect mycosis fungoides (MF), psoriasis (PSO), and atopic dermatitis (AD) is demonstrated. Results showed that 10 s was consumed by the single shot multibox detector (SSD) model to analyze 292 test images, among which 273 images were correctly detected. Verification of ground truth samples of this research come from pathological tissue slices and OCT analysis. The SSD diagnosis accuracy rate was 93%. The sensitivity values of the SSD model in diagnosing the skin lesions according to the symptoms of PSO, AD, MF, and normal were 96%, 80%, 94%, and 95%, and the corresponding precision were 96%, 86%, 98%, and 90%. The highest sensitivity rate was found in MF probably because of the spread of cancer cells in the skin and relatively large lesions of MF. Many differences were found in the accuracy between AD and the other diseases. The collected AD images were all in the elbow or arm and other joints, the area with AD was small, and the features were not obvious. Hence, the proposed SSD could be used to identify the four diseases by using skin image detection, but the diagnosis of AD was relatively poor. |
format | Online Article Text |
id | pubmed-7796252 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-77962522021-01-10 Identification of Skin Lesions by Using Single-Step Multiframe Detector Hsiao, Yu-Ping Chiu, Chih-Wei Lu, Chih-Wei Nguyen, Hong Thai Tseng, Yu Sheng Hsieh, Shang-Chin Wang, Hsiang-Chen J Clin Med Article An artificial intelligence algorithm to detect mycosis fungoides (MF), psoriasis (PSO), and atopic dermatitis (AD) is demonstrated. Results showed that 10 s was consumed by the single shot multibox detector (SSD) model to analyze 292 test images, among which 273 images were correctly detected. Verification of ground truth samples of this research come from pathological tissue slices and OCT analysis. The SSD diagnosis accuracy rate was 93%. The sensitivity values of the SSD model in diagnosing the skin lesions according to the symptoms of PSO, AD, MF, and normal were 96%, 80%, 94%, and 95%, and the corresponding precision were 96%, 86%, 98%, and 90%. The highest sensitivity rate was found in MF probably because of the spread of cancer cells in the skin and relatively large lesions of MF. Many differences were found in the accuracy between AD and the other diseases. The collected AD images were all in the elbow or arm and other joints, the area with AD was small, and the features were not obvious. Hence, the proposed SSD could be used to identify the four diseases by using skin image detection, but the diagnosis of AD was relatively poor. MDPI 2021-01-04 /pmc/articles/PMC7796252/ /pubmed/33406761 http://dx.doi.org/10.3390/jcm10010144 Text en © 2021 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Hsiao, Yu-Ping Chiu, Chih-Wei Lu, Chih-Wei Nguyen, Hong Thai Tseng, Yu Sheng Hsieh, Shang-Chin Wang, Hsiang-Chen Identification of Skin Lesions by Using Single-Step Multiframe Detector |
title | Identification of Skin Lesions by Using Single-Step Multiframe Detector |
title_full | Identification of Skin Lesions by Using Single-Step Multiframe Detector |
title_fullStr | Identification of Skin Lesions by Using Single-Step Multiframe Detector |
title_full_unstemmed | Identification of Skin Lesions by Using Single-Step Multiframe Detector |
title_short | Identification of Skin Lesions by Using Single-Step Multiframe Detector |
title_sort | identification of skin lesions by using single-step multiframe detector |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7796252/ https://www.ncbi.nlm.nih.gov/pubmed/33406761 http://dx.doi.org/10.3390/jcm10010144 |
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