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Related Concept Videos

Skin Cancer01:30

Skin Cancer

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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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The first thing a clinician sees is the skin, so the examination of the skin should be part of any thorough physical examination. Most skin disorders are relatively benign, but a few, including melanomas, can be fatal if untreated. A couple of the more noticeable disorders, albinism and vitiligo, affect the appearance of the skin and its accessory organs.
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Related Experiment Video

Updated: Jul 10, 2025

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition
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Automatic Skin Cancer Detection Using Clinical Images: A Comprehensive Review.

Sana Nazari1, Rafael Garcia1

  • 1Computer Vision and Robotics Group, University of Girona, 17003 Girona, Spain.

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|November 25, 2023
PubMed
Summary
This summary is machine-generated.

Early skin cancer detection using machine learning on clinical images is crucial. This review highlights the need for better clinical datasets and models that analyze mole patterns over time.

Keywords:
automated diagnosis of pigmented skin lesions (PSLs), computer-aided diagnosisclinical skin imagesliterature reviewmelanoma detectionskin cancer detection

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Area of Science:

  • Dermatology
  • Computer Science
  • Medical Imaging

Background:

  • Skin cancer, particularly melanoma, is a growing concern, necessitating early detection.
  • Machine learning (ML) shows promise for skin cancer identification, but most research uses dermoscopy images.
  • General practitioners often lack dermoscopes, relying on standard clinical images for diagnosis.

Purpose of the Study:

  • To comprehensively review image-processing techniques for skin cancer detection using clinical images.
  • To evaluate 51 recent articles focusing on ML methods for skin cancer detection in clinical datasets.

Main Methods:

  • Systematic review and analysis of 51 state-of-the-art research articles.
  • Focus on studies utilizing machine learning for skin cancer detection from clinical images.

Main Results:

  • Few publicly available clinical image datasets exist for benchmarking compared to dermoscopy datasets.
  • Current artifact removal techniques in ML models can be inadequate and negatively impact performance.
  • Most studies analyze single-lesion images, neglecting patient-specific mole patterns and temporal changes.

Conclusions:

  • There is a significant need for larger, standardized clinical image datasets for robust ML model development.
  • Future research should address artifact removal challenges and incorporate longitudinal data for improved skin cancer detection accuracy.