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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.
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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An efficient multi-class classification of skin cancer using optimized vision transformer.

R P Desale1, P S Patil2

  • 1E&TC Engineering Department, SSVPS's Bapusaheb Shivajirao Deore College of Engineering, Dhule, Maharashtra, 424005, India. desale.rajendra@gmail.com.

Medical & Biological Engineering & Computing
|November 23, 2023
PubMed
Summary

This study presents an optimized vision transformer for skin cancer classification, achieving 99.81% accuracy. The advanced method effectively identifies skin tumors from images, outperforming existing techniques.

Keywords:
Gradient intensity extractionHybrid Walsh Hadamard feature extractionImproved vision transformerNoise filteringPiecewise linear bottom hat filteringSelf-sparse watershed segmentationSkin cancer

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Area of Science:

  • Dermatology
  • Computer Science
  • Medical Imaging

Background:

  • Skin cancer is a significant global health concern.
  • Accurate and early detection of skin malignancies is crucial.
  • Computer-based analysis of skin lesion images offers a promising approach for diagnosis.

Purpose of the Study:

  • To develop and evaluate an optimized vision transformer model for accurate skin cancer classification.
  • To improve the identification of skin tumors using advanced image analysis techniques.

Main Methods:

  • Image pre-processing including color constancy, hair artifact removal, and noise reduction using filters (piecewise linear bottom hat, adaptive median, Gaussian) and gradient intensity.
  • Image segmentation using the self-sparse watershed algorithm.
  • Feature extraction via hybrid Walsh-Hadamard Karhunen-Loeve expansion.
  • Skin cancer classification using an improved vision transformer.

Main Results:

  • The proposed methodology achieved high performance metrics: 99.81% accuracy, 96.65% precision, 98.21% sensitivity, 97.42% F-measure, 99.88% specificity, 98.21% recall, 98.54% Jaccard coefficient, and 98.89% MCC.
  • The system demonstrated superior performance compared to existing methodologies.
  • Experiments were conducted on the International Skin Imaging Collaboration (ISIC) 2019 database.

Conclusions:

  • The optimized vision transformer approach provides a highly effective and accurate method for skin cancer classification.
  • The integrated methodology, combining advanced pre-processing, segmentation, and feature extraction, significantly enhances diagnostic capabilities.
  • This research contributes a robust computational tool for aiding dermatologists in skin tumor identification.