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...
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doaj-602868ca95bd4becb93285825d0fd9612021-01-05T00:03:18ZengMDPI AGJournal of Clinical Medicine2077-03832021-01-011014414410.3390/jcm10010144Identification of Skin Lesions by Using Single-Step Multiframe DetectorYu-Ping Hsiao0Chih-Wei Chiu1Chih-Wei Lu2Hong Thai Nguyen3Yu Sheng Tseng4Shang-Chin Hsieh5Hsiang-Chen Wang6Department of Dermatology, Chung Shan Medical University Hospital, No.110, Sec. 1, Jianguo N. Rd., South Dist., Taichung City 40201, TaiwanDepartment of Mechanical Engineering and Center for Innovative Research on Aging Society (CIRAS), National Chung Cheng University, 168, University Rd., Min Hsiung, Chia Yi 62102, TaiwanDirector of Technology Development, Apollo Medical Optics, Inc. (AMO), 2F., No. 43, Ln. 188, Ruiguang Rd., Neihu Dist., Taipei City 114, TaiwanDepartment of Mechanical Engineering and Center for Innovative Research on Aging Society (CIRAS), National Chung Cheng University, 168, University Rd., Min Hsiung, Chia Yi 62102, TaiwanDepartment of Mechanical Engineering and Center for Innovative Research on Aging Society (CIRAS), National Chung Cheng University, 168, University Rd., Min Hsiung, Chia Yi 62102, TaiwanDepartment of Plastic Surgery, Kaohsiung Armed Forces General Hospital, 2, Zhongzheng 1st.Rd., Lingya District, Kaohsiung City 80284, TaiwanDepartment of Mechanical Engineering and Center for Innovative Research on Aging Society (CIRAS), National Chung Cheng University, 168, University Rd., Min Hsiung, Chia Yi 62102, TaiwanAn 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.https://www.mdpi.com/2077-0383/10/1/144mycosis fungoidessingle shot multibox detectorpsoriasisatopic dermatitisoptical coherence tomography |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yu-Ping Hsiao Chih-Wei Chiu Chih-Wei Lu Hong Thai Nguyen Yu Sheng Tseng Shang-Chin Hsieh Hsiang-Chen Wang |
spellingShingle |
Yu-Ping Hsiao Chih-Wei Chiu Chih-Wei Lu Hong Thai Nguyen Yu Sheng Tseng Shang-Chin Hsieh Hsiang-Chen Wang Identification of Skin Lesions by Using Single-Step Multiframe Detector Journal of Clinical Medicine mycosis fungoides single shot multibox detector psoriasis atopic dermatitis optical coherence tomography |
author_facet |
Yu-Ping Hsiao Chih-Wei Chiu Chih-Wei Lu Hong Thai Nguyen Yu Sheng Tseng Shang-Chin Hsieh Hsiang-Chen Wang |
author_sort |
Yu-Ping Hsiao |
title |
Identification of Skin Lesions by Using Single-Step Multiframe Detector |
title_short |
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_sort |
identification of skin lesions by using single-step multiframe detector |
publisher |
MDPI AG |
series |
Journal of Clinical Medicine |
issn |
2077-0383 |
publishDate |
2021-01-01 |
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. |
topic |
mycosis fungoides single shot multibox detector psoriasis atopic dermatitis optical coherence tomography |
url |
https://www.mdpi.com/2077-0383/10/1/144 |
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