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Skin Cancer01:30

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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.
Basal Cell Carcinoma (BCC): BCC is the most common type of skin cancer, accounting for about 80% of cases. It typically develops in...
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Detection of Vitiligo Through Machine Learning and Computer-Aided Techniques: A Systematic Review.

Sania Tanvir1, Sidra Abid Syed1, Samreen Hussain2

  • 1Faculty of Electrical and Computer Engineering, Biomedical Engineering Department, Sir Syed University of Engineering and Technology, Karachi, Pakistan.

Biomed Research International
|December 30, 2024
PubMed
Summary
This summary is machine-generated.

Machine learning (ML) techniques show promise for accurate vitiligo diagnosis, reducing human bias. Further research is needed to explore ML algorithms with diverse datasets for improved detection and diagnosis of this chronic skin condition.

Keywords:
detectiondiagnosisimage segmentationmachine learningskin diseasesystematic reviewvitiligo

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Area of Science:

  • Dermatology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Vitiligo is a chronic skin condition affecting 0.5%-1% of the population, characterized by melanocyte death and patchy skin depigmentation.
  • Accurate diagnosis is crucial for effective management of vitiligo.
  • Current diagnostic methods can be subjective and prone to human judgment bias.

Conclusions:

  • Machine learning holds significant potential for enhancing the accuracy of vitiligo diagnosis.
  • ML application can minimize subjective human judgment in clinical assessments.
  • Further research is recommended to investigate ML algorithms with diverse datasets and advanced feature extraction for improved vitiligo detection.