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Article

Identification of Skin Lesions by Using Single-Step Multiframe Detector

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Department of Dermatology, Chung Shan Medical University Hospital, No.110, Sec. 1, Jianguo N. Rd., South Dist., Taichung City 40201, Taiwan
2
Institute of Medicine, School of Medicine, Chung Shan Medical University, No.110, Sec. 1, Jianguo N. Rd., South Dist., Taichung City 40201, Taiwan
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Department of Mechanical Engineering and Center for Innovative Research on Aging Society (CIRAS), National Chung Cheng University, 168, University Rd., Min Hsiung, Chia Yi 62102, Taiwan
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Director of Technology Development, Apollo Medical Optics, Inc. (AMO), 2F., No. 43, Ln. 188, Ruiguang Rd., Neihu Dist., Taipei City 114, Taiwan
5
Department of Plastic Surgery, Kaohsiung Armed Forces General Hospital, 2, Zhongzheng 1st.Rd., Lingya District, Kaohsiung City 80284, Taiwan
*
Authors to whom correspondence should be addressed.
J. Clin. Med. 2021, 10(1), 144; https://doi.org/10.3390/jcm10010144
Received: 27 October 2020 / Revised: 23 December 2020 / Accepted: 30 December 2020 / Published: 4 January 2021
(This article belongs to the Special Issue Chronic Inflammatory Skin Diseases: An Update for Clinician)
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. View Full-Text
Keywords: mycosis fungoides; single shot multibox detector; psoriasis; atopic dermatitis; optical coherence tomography mycosis fungoides; single shot multibox detector; psoriasis; atopic dermatitis; optical coherence tomography
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MDPI and ACS Style

Hsiao, Y.-P.; Chiu, C.-W.; Lu, C.-W.; Nguyen, H.T.; Tseng, Y.S.; Hsieh, S.-C.; Wang, H.-C. Identification of Skin Lesions by Using Single-Step Multiframe Detector. J. Clin. Med. 2021, 10, 144. https://doi.org/10.3390/jcm10010144

AMA Style

Hsiao Y-P, Chiu C-W, Lu C-W, Nguyen HT, Tseng YS, Hsieh S-C, Wang H-C. Identification of Skin Lesions by Using Single-Step Multiframe Detector. Journal of Clinical Medicine. 2021; 10(1):144. https://doi.org/10.3390/jcm10010144

Chicago/Turabian Style

Hsiao, Yu-Ping, Chih-Wei Chiu, Chih-Wei Lu, Hong Thai Nguyen, Yu Sheng Tseng, Shang-Chin Hsieh, and Hsiang-Chen Wang. 2021. "Identification of Skin Lesions by Using Single-Step Multiframe Detector" Journal of Clinical Medicine 10, no. 1: 144. https://doi.org/10.3390/jcm10010144

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