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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 image segmentation method for skin cancer imaging using improved golden jackal optimization algorithm.

Essam H Houssein1, Doaa A Abdelkareem2, Marwa M Emam1

  • 1Faculty of Computers and Information, Minia University, Minia, Egypt.

Computers in Biology and Medicine
|September 17, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an improved golden jackal optimizer (IGJO) for enhanced skin cancer image segmentation. The new method, IGJO, significantly improves early diagnosis and classification accuracy for skin cancer detection.

Keywords:
Golden jackal optimization algorithmImage processingMeta-heuristic algorithmsOtsu’s methodSkin cancer

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Area of Science:

  • Medical Imaging
  • Computational Intelligence
  • Dermatology

Background:

  • Skin cancer poses a significant global health threat, necessitating accurate early diagnosis.
  • Image segmentation is crucial for extracting relevant features from skin lesion images to improve classification accuracy.
  • Existing meta-heuristic algorithms have limitations in effectively segmenting complex skin cancer images.

Purpose of the Study:

  • To propose an efficient opposition-based golden jackal optimizer (IGJO) for multilevel thresholding in skin cancer image segmentation.
  • To evaluate the performance of IGJO against several state-of-the-art meta-heuristic algorithms.
  • To enhance the accuracy and efficiency of skin cancer classification through improved image segmentation.

Main Methods:

  • Implementation of the opposition-based golden jackal optimizer (IGJO) algorithm.
  • Application of IGJO to solve the multilevel thresholding problem using Otsu's method as the objective function.
  • Comparative analysis of IGJO with seven other meta-heuristic algorithms (WOA, SOA, SSA, HHO, GTO, MPA, GJO) using standard performance metrics.

Main Results:

  • The proposed IGJO algorithm demonstrated superior performance compared to the other seven algorithms.
  • IGJO achieved better results in terms of peak signal-to-noise ratio (PSNR), structure similarity index (SSIM), feature similarity index (FSIM), and mean square error (MSE).
  • The algorithm effectively resolved the segmentation challenges in skin cancer images.

Conclusions:

  • The opposition-based golden jackal optimizer (IGJO) is an efficient and effective method for multilevel thresholding in skin cancer image segmentation.
  • IGJO offers a promising approach for improving the accuracy of early skin cancer diagnosis and classification.
  • This research contributes to the advancement of computational intelligence techniques in medical image analysis for cancer detection.