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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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Correction: Luca et al. Global and Regional Diagnostic Results of Progress Toward Cervical Cancer Elimination, According to the WHO Strategy: A Systematic Literature Review with Narrative Synthesis. <i>Diagnostics</i> 2026, <i>16</i>, 1224.

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Updated: Jul 15, 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

Multilevel Threshold Segmentation of Skin Lesions in Color Images Using Coronavirus Optimization Algorithm.

Yousef S Alsahafi¹, Doaa S Elshora², Ehab R Mohamed²

¹Department of Information Technology, Khulis College, University of Jeddah, Jeddah 23890, Saudi Arabia.

Diagnostics (Basel, Switzerland)

|September 28, 2023

Summary

This summary is machine-generated.

This study introduces a novel Coronavirus Disease Optimization Algorithm (COVIDOA) for enhanced early skin cancer detection through medical image segmentation. The optimized algorithm significantly improves segmentation accuracy, aiding in timely diagnosis and treatment.

Keywords:

COVIDOA Kapur Otsu Tsallis image segmentation skin cancer images

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Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition

Published on: August 18, 2022

Area of Science:

Medical Imaging
Computational Intelligence
Dermatology

Background:

Skin cancer (SC) poses a significant health risk due to its high mortality rate.
Early detection of skin cancer is crucial for effective treatment outcomes.
Multilevel Thresholding (MLT) is a key technique for segmenting regions of interest in medical images.

Purpose of the Study:

To develop and evaluate a novel meta-heuristic algorithm for improved skin cancer image segmentation.
To address the Multilevel Thresholding (MLT) challenges in segmenting skin cancer images.
To enhance the early detection capabilities for skin cancer through advanced image analysis.

Main Methods:

Utilized the Coronavirus Disease Optimization Algorithm (COVIDOA) for Multilevel Thresholding (MLT) of skin cancer images.
Integrated Otsu, Kapur, and Tsallis methods as fitness functions within the COVIDOA framework.
Compared the proposed COVIDOA against Arithmetic Optimization Algorithm (AOA), Sine Cosine Algorithm (SCA), Reptile Search Algorithm (RSA), Flower Pollination Algorithm (FPA), Seagull Optimization Algorithm (SOA), and Artificial Gorilla Troops Optimizer (GTO).

Main Results:

The proposed COVIDOA algorithm demonstrated superior performance in segmenting skin cancer images compared to existing meta-heuristic algorithms.
Achieved lower Mean Square Error (MSE) and higher Peak Signal-To-Noise Ratio (PSNR), indicating improved image segmentation quality.
Showcased enhanced Feature Similarity Index Metric (FSIM) and Normalized Correlation Coefficient (NCC) values, confirming segmentation accuracy.

Conclusions:

The hybrid COVIDOA approach effectively solves the Multilevel Thresholding (MLT) problem for skin cancer image segmentation.
The algorithm's superior performance in key segmentation metrics (MSE, PSNR, FSIM, NCC) supports its clinical utility.
This advancement in image segmentation technology holds promise for improving early skin cancer detection and patient outcomes.