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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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Machine learning based skin lesion segmentation method with novel borders and hair removal techniques.

Mohibur Rehman1, Mushtaq Ali1, Marwa Obayya2

  • 1Department of Computer Science & Information Technology, Hazara University, Mansehra, Pakistan.

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This study introduces an improved skin lesion segmentation method for dermoscopic images, enhancing computer-aided diagnosis (CAD) systems. The new technique effectively removes artifacts like hairs and borders, leading to more accurate skin cancer detection.

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Area of Science:

  • Dermatology
  • Medical Imaging
  • Computer Science

Background:

  • Accurate skin lesion segmentation is crucial for computer-aided diagnosis (CAD) of skin cancer.
  • Existing segmentation methods struggle with artifacts in dermoscopic images, such as hair, varying borders, and low contrast.
  • These artifacts reduce the diagnostic accuracy of CAD systems.

Purpose of the Study:

  • To develop an effective skin lesion segmentation method for dermoscopic images, addressing limitations of current techniques.
  • To improve the performance of CAD systems by enhancing the accuracy of skin lesion identification.
  • To accurately segment skin lesions despite the presence of challenging artifacts.

Main Methods:

  • A novel method was proposed to first detect and remove artifacts like corner borders and hairs from dermoscopic images.
  • The artifact-removed images were then enhanced using a state-of-the-art image enhancement technique.
  • Lesion segmentation was performed using the GrabCut machine learning algorithm.

Main Results:

  • The proposed method achieved a Jaccard Index of 0.77 on the PH2 dataset and 0.80 on the ISIC 2018 dataset.
  • The Dice Index scores were 0.87 for PH2 and 0.82 for ISIC 2018.
  • These results indicate superior performance compared to existing state-of-the-art skin lesion segmentation techniques.

Conclusions:

  • The proposed method effectively segments skin lesions from dermoscopic images, even in the presence of artifacts.
  • The technique shows significant improvements over existing methods, paving the way for more reliable CAD systems.
  • This advancement can lead to earlier and more accurate diagnosis of skin cancer.