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Skin cancer is a type of cancer that occurs when there is an abnormal growth of skin cells, usually triggered by damage to the DNA within the skin cells. It is primarily caused by exposure to ultraviolet (UV) radiation from the sun or artificial sources like tanning beds. Skin cancer is the most common type of cancer worldwide, and its incidence continues to rise.
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Related Experiment Video

Updated: Dec 5, 2025

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition
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Deep Learning for Diagnostic Binary Classification of Multiple-Lesion Skin Diseases.

Kenneth Thomsen1, Anja Liljedahl Christensen2, Lars Iversen1

  • 1Department of Dermatology and Venereology, Aarhus University Hospital, Aarhus, Denmark.

Frontiers in Medicine
|October 19, 2020
PubMed
Summary

A new convolutional neural network model accurately classifies multiple skin diseases, including acne, rosacea, psoriasis, eczema, and cutaneous T-cell lymphoma. This AI tool shows diagnostic performance comparable to trained dermatologists.

Keywords:
acnecutaneous T cell lymphoma (CTCL)deep neural network (DNN)dermatologyezcemapsoriasisrosaceaskin disease

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Area of Science:

  • Dermatology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Accurate diagnosis of skin conditions is challenging.
  • Computer-aided diagnostic tools are crucial for clinical decision-making.
  • Developing AI for dermatology can improve diagnostic accuracy.

Purpose of the Study:

  • To develop a convolutional neural network (CNN) model for classifying multiple skin diseases.
  • The model adheres to STARD (Standards for Reporting Diagnostic Accuracy) guidelines.
  • The study aimed to classify selected clinically relevant skin diseases with multiple lesions.

Main Methods:

  • An image-based retrospective study utilizing multi-task learning for binary classification.
  • A VGG-16 convolutional neural network model was trained on 16,543 non-standardized images.
  • Data was sourced from a Danish clinical database, including patients with ICD-10 codes for acne, rosacea, psoriasis, eczema, and cutaneous T-cell lymphoma.

Main Results:

  • Acne distinguished from rosacea: 85.42% sensitivity, 89.53% specificity.
  • Cutaneous T-cell lymphoma distinguished from eczema: 74.29% sensitivity, 84.09% specificity.
  • Psoriasis distinguished from eczema: 81.79% sensitivity, 73.57% specificity.

Conclusions:

  • The CNN model's performance meets or exceeds that of general practitioners with dermatological training.
  • AI-based diagnostic models show potential for diagnosing multiple-lesion skin diseases.
  • Computer-aided diagnosis in dermatology can support clinical practice.