Identification of Skin Lesions by Using Single-Step Multiframe Detector

doi:10.3390/jcm10010144

中山醫學大學機構典藏 CSMUIR > 中山醫學大學研究成果 > 期刊論文 > Item 310902500/23296

Please use this identifier to cite or link to this item: https://ir.csmu.edu.tw:8080/ir/handle/310902500/23296

Title:	Identification of Skin Lesions by Using Single-Step Multiframe Detector
Authors:	Hsiao, YP;Chiu, CW;Lu, CW;Nguyen, HT;Tseng, YS;Hsieh, SC;Wang, HC
Keywords:	mycosis fungoides;single shot multibox detector;psoriasis;atopic dermatitis;optical coherence tomography
Date:	2021
Issue Date:	2022-08-05T09:35:00Z (UTC)
Publisher:	MDPI
Abstract:	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.
URI:	http://dx.doi.org/10.3390/jcm10010144 https://www.webofscience.com/wos/woscc/full-record/WOS:000606104200001 https://ir.csmu.edu.tw:8080/handle/310902500/23296
Relation:	JOURNAL OF CLINICAL MEDICINE ,2021,v10,issue 1
Appears in Collections:	[中山醫學大學研究成果] 期刊論文

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