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Updated: Nov 22, 2025

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging
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Identification of Skin Lesions by Using Single-Step Multiframe Detector.

Yu-Ping Hsiao1,2, Chih-Wei Chiu3, Chih-Wei Lu4

  • 1Department of Dermatology, Chung Shan Medical University Hospital, No.110, Sec. 1, Jianguo N. Rd., South Dist., Taichung City 40201, Taiwan.

Journal of Clinical Medicine
|January 7, 2021
PubMed
Summary

An artificial intelligence algorithm achieved 93% accuracy in detecting skin conditions like mycosis fungoides (MF) and psoriasis (PSO), though atopic dermatitis (AD) diagnosis was less accurate.

Keywords:
atopic dermatitismycosis fungoidesoptical coherence tomographypsoriasissingle shot multibox detector

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Area of Science:

  • Dermatology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Accurate and timely diagnosis of skin conditions is crucial for effective treatment.
  • Dermatological conditions such as mycosis fungoides (MF), psoriasis (PSO), and atopic dermatitis (AD) share some visual similarities, posing diagnostic challenges.
  • Artificial intelligence (AI) offers potential for automated analysis of medical images.

Purpose of the Study:

  • To develop and evaluate an AI algorithm for detecting MF, PSO, and AD from skin images.
  • To assess the diagnostic accuracy, sensitivity, and precision of the AI model for these conditions.

Main Methods:

  • A single shot multibox detector (SSD) AI model was trained and tested on skin images.
  • Ground truth for verification was established using pathological tissue slices and Optical Coherence Tomography (OCT) analysis.
  • The model analyzed 292 test images, with performance metrics including accuracy, sensitivity, and precision calculated for each condition.

Main Results:

  • The SSD model achieved an overall diagnostic accuracy of 93% across 273 correctly detected images out of 292.
  • High sensitivity and precision were observed for MF (94-98%) and PSO (96%).
  • Atopic dermatitis (AD) showed lower diagnostic performance (80% sensitivity, 86% precision) due to small lesion size and indistinct features in the analyzed dataset.

Conclusions:

  • The proposed SSD algorithm demonstrates significant potential for identifying MF, PSO, and AD using skin image analysis.
  • While effective for MF and PSO, the AI model's diagnostic capability for AD requires improvement, potentially through larger and more diverse datasets.
  • Further refinement of AI algorithms is warranted to enhance the detection of challenging dermatological conditions like AD.