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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 3, 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 Improved Skin Lesion Boundary Estimation for Enhanced-Intensity Images Using Hybrid Metaheuristics.

Shairyar Malik¹, Tallha Akram¹, Muhammad Awais¹

¹Department of Electrical and Computer Engineering, Wah Campus, COMSATS University Islamabad, Wah Cantt 47040, Pakistan.

Diagnostics (Basel, Switzerland)

|April 13, 2023

Summary

This summary is machine-generated.

This study introduces BA-ABC, a novel hybrid metaheuristic preprocessor for enhancing skin lesion image quality. The method improves contrast and brightness, boosting segmentation accuracy for melanoma identification.

Keywords:

artificial bee colony bat algorithm computer vision deep learning machine learning skin lesion segmentation

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Area of Science:

Dermatology
Computer Vision
Medical Image Analysis

Background:

Accurate melanoma identification is crucial for effective skin cancer treatment.
Computer vision and image segmentation are vital for analyzing skin lesions.
Existing methods often require further improvement in lesion segmentation from background.

Purpose of the Study:

To propose a hybrid metaheuristic preprocessor, BA-ABC, for enhancing skin lesion image quality.
To improve contrast and preserve brightness in medical images for better segmentation.
To validate the effectiveness of the proposed preprocessor with state-of-the-art segmentation algorithms.

Main Methods:

Development of a hybrid metaheuristic preprocessor named BA-ABC.
Utilizing a statistical transformation function for contrast enhancement.
Estimating optimal parameters using the BA-ABC model for each image.
Experimentation on publicly available datasets: ISIC-2016, 2017, and 2018.

Main Results:

The BA-ABC preprocessor effectively enhances image contrast while preserving brightness.
Validation using state-of-the-art segmentation algorithms demonstrated significant improvements.
The proposed model achieved a Dice coefficient of 94.6% in segmentation tasks.

Conclusions:

The BA-ABC hybrid metaheuristic preprocessor is effective in improving skin lesion image quality.
Enhanced image quality leads to superior performance in segmentation algorithms.
This approach shows promise for advancing automated melanoma detection and analysis.