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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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Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging
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Skin Cancer Image Segmentation Based on Midpoint Analysis Approach.

Uzma Saghir1, Shailendra Kumar Singh2, Moin Hasan3

  • 1Dept. of Computer Science & Engineering, Lovely Professional University, Punjab, 144001, India.

Journal of Imaging Informatics in Medicine
|April 16, 2024
PubMed
Summary

Early skin cancer detection is crucial. This study presents an automated segmentation method for dermoscopic images, achieving 95.30% accuracy for early lesion identification and reducing mortality rates.

Keywords:
Background subtractionHair removalImage enhancementSegmentationSkin cancer

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Area of Science:

  • Dermatology
  • Medical Imaging
  • Computer Vision

Background:

  • Skin cancer is a prevalent disease with increasing mortality due to late diagnosis.
  • Current visual examination methods for skin cancer detection lack accuracy due to lesion similarity to other conditions.
  • Automated early-stage detection is essential to reduce skin cancer-related deaths.

Purpose of the Study:

  • To develop an innovative segmentation mechanism for early-stage skin cancer detection.
  • To accurately segment lesions from dermoscopic skin images using the ISIC dataset.
  • To improve the accuracy of skin lesion analysis for timely diagnosis.

Main Methods:

  • A two-step framework was implemented for image segmentation.
  • Image preprocessing involved a bottom hat filter for hair removal and DCT/color coefficient for enhancement.
  • A background subtraction method with midpoint analysis was used for segmenting regions of interest.

Main Results:

  • The proposed segmentation mechanism achieved an accuracy of 95.30%.
  • The method effectively segmented lesions from dermoscopic images in the ISIC dataset.
  • Validation against ground truth confirmed the segmentation accuracy.

Conclusions:

  • The developed automated segmentation mechanism shows high accuracy for early skin cancer detection.
  • This approach can aid in diminishing mortality rates through timely diagnosis.
  • The method offers a promising tool for analyzing dermoscopic images in clinical settings.