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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.
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 Lesion Segmentation Using an Ensemble of Different Image Processing Methods.

Maria Tamoor1, Asma Naseer2, Ayesha Khan1

  • 1Department of Computer Science, Forman Christian College, Lahore 54600, Pakistan.

Diagnostics (Basel, Switzerland)
|August 26, 2023
PubMed
Summary
This summary is machine-generated.

An ensemble method improves skin lesion segmentation in dermoscopic images by combining thresholding techniques. This approach enhances early skin cancer detection by overcoming image artefacts, achieving a superior dice score of 0.89.

Keywords:
CADdermoscopyensemblepreprocessingthresholding

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Area of Science:

  • Dermatology
  • Medical Imaging
  • Computer Vision

Background:

  • Skin cancer cases are rising, making early detection critical.
  • Dermoscopic images contain artefacts (hair, markers, poor boundaries) that hinder automated analysis.
  • Existing segmentation methods struggle with diverse skin lesion artefacts.

Purpose of the Study:

  • To develop an accurate and efficient automated method for skin lesion segmentation.
  • To overcome limitations of single thresholding methods in handling image artefacts.
  • To improve early detection of skin diseases through precise lesion delineation.

Main Methods:

  • Proposed an ensemble-based method for skin lesion segmentation.
  • Optimized threshold selection using an objective function.
  • Integrated multiple state-of-the-art thresholding algorithms (Otsu, Kapur, Harris hawk, grey level).

Main Results:

  • The proposed ensemble method achieved a superior dice score of 0.89 (p ≤ 0.05).
  • Outperformed individual methods: Otsu (0.79), Kapur (0.80), Harris hawk (0.60), grey level (0.69), and active contour model (0.72).
  • Demonstrated effectiveness on the ISIC 2016 dataset.

Conclusions:

  • Ensemble-based segmentation effectively addresses artefacts in dermoscopic images.
  • The proposed method offers a significant improvement over existing techniques for skin lesion analysis.
  • Accurate segmentation is vital for advancing automated skin disease diagnosis and early detection.