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Skin Cancer

Skin Cancer

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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Related Experiment Video

Updated: Aug 22, 2025

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Published on: May 5, 2011

An efficient multi-threshold image segmentation for skin cancer using boosting whale optimizer.

Wei Zhu¹, Lei Liu², Fangjun Kuang³

¹School of Resources and Safety Engineering, Central South University, Changsha, 410083, China.

Computers in Biology and Medicine

|November 11, 2022

Summary

This summary is machine-generated.

This study introduces an improved Whale Optimization Algorithm (LCWOA) for enhanced skin cancer image segmentation. LCWOA improves early detection by optimizing threshold selection, leading to better diagnostic accuracy.

Keywords:

Chaotic random mutation strategy Image segmentation Kapur's entropy Levy operator Skin cancer Whale optimization algorithm

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Area of Science:

Medical Image Analysis
Computational Intelligence
Dermatology

Background:

Skin cancer significantly impacts patient quality of life, necessitating early detection and effective treatment strategies.
Medical image segmentation, particularly thresholding, is crucial for identifying regions of interest in disease diagnosis.
Metaheuristic algorithms offer advantages in threshold selection but exhibit variable performance across different medical imaging applications.

Purpose of the Study:

To develop an improved metaheuristic algorithm for accurate multi-threshold image segmentation in medical imaging.
To enhance the performance of the Whale Optimization Algorithm (WOA) for skin cancer image thresholding.
To improve the accuracy and efficiency of skin cancer detection through optimized image segmentation.

Main Methods:

Proposed a novel algorithm, Levy-Chaotic Whale Optimization Algorithm (LCWOA), by integrating Levy operator and chaotic random mutation into WOA.
Evaluated LCWOA's search efficiency and optimization performance on the CEC2014 benchmark function set against existing WOA variants.
Applied the LCWOA to the problem of threshold selection for skin cancer image segmentation.

Main Results:

LCWOA demonstrated superior search efficiency, convergence accuracy, and velocity compared to existing WOA variants on the CEC2014 test functions.
The proposed LCWOA achieved excellent performance in obtaining optimal segmentation results for skin cancer images.
The enhanced algorithm effectively overcomes local optima and improves exploration of the search space.

Conclusions:

The LCWOA algorithm represents a significant advancement in metaheuristic optimization for medical image analysis.
This method provides a robust and efficient solution for threshold selection in skin cancer image segmentation.
LCWOA holds promise for improving early skin cancer detection and patient outcomes.