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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.
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Updated: Nov 15, 2025

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Psoriasis Lesion Detection Using Hybrid Seeker Optimization-based Image Clustering.

Manoranjan Dash¹, Narendra Digambar Londhe¹, Subhojit Ghosh¹

¹Electrical Engineering Department, National Institute of Technology, Raipur 492010, India.

Current Medical Imaging

|March 3, 2021

Summary

This summary is machine-generated.

A new hybrid evolutionary algorithm improves psoriasis lesion segmentation for objective disease severity assessment. This method enhances diagnostic accuracy by overcoming limitations of traditional clustering techniques.

Keywords:

Image segmentation artificial bee colony clustering fuzzy C-means hybrid seeker optimization.psoriasis seeker optimization

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Area of Science:

Dermatology
Medical Imaging
Computational Intelligence

Background:

Psoriasis prevalence is increasing, necessitating accurate methods for disease severity quantification.
Psoriatic lesion segmentation is crucial for objective diagnosis, but visual inspection is subjective due to lesion variability.
Conventional clustering algorithms for segmentation suffer from local convergence issues, limiting their effectiveness.

Purpose of the Study:

To develop an objective method for psoriatic lesion segmentation using evolutionary optimization.
To achieve optimal lesion segmentation with a higher probability of global convergence.

Main Methods:

A hybrid evolutionary optimization technique combining Artificial Bee Colony and Seeker Optimization algorithms was proposed.
Initial populations were derived from Fuzzy C-means and K-means clustering algorithms.
This approach aimed to mitigate the impact of population dynamics on segmentation accuracy.

Main Results:

The proposed algorithm achieved precise lesion segmentation with a Jaccard Index of 0.91 on 720 psoriasis images.
Initial population selection from classical techniques improved the final segmentation outcome.
The method demonstrated superior performance compared to other swarm intelligence and clustering algorithms.

Conclusions:

The developed hybrid evolutionary algorithm offers a more objective and accurate approach to psoriatic lesion segmentation.
This technique addresses the limitations of conventional methods, improving diagnostic capabilities for psoriasis.
The findings suggest a significant advancement in computational dermatology for disease assessment.