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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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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

Henry Gas Solubility Optimization Algorithm based Feature Extraction in Dermoscopic Images Analysis of Skin Cancer.

Marwa Obayya¹, Adeeb Alhebri², Mashael Maashi³

¹Department of Biomedical Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia.

|April 13, 2023

Summary

This summary is machine-generated.

Artificial Intelligence (AI) techniques, including Convolutional Neural Networks (CNN), are revolutionizing skin cancer diagnosis. The proposed Optimal Multi-Attention Fusion Convolutional Neural Network-based Skin Cancer Diagnosis (MAFCNN-SCD) technique shows superior performance in classifying skin cancer from dermoscopic images.

Keywords:

MAFNet computer aided diagnosis dermoscopic images metaheuristics skin cancer

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Published on: September 26, 2019

Area of Science:

Medical Diagnostics
Artificial Intelligence
Dermatology

Background:

Dermatologists traditionally diagnose skin conditions through visual inspection of morphological variables.
Artificial Intelligence (AI) techniques, particularly Deep Learning (DL) and Machine Learning (ML), are increasingly applied to medical diagnostics.
AI assists clinicians by mimicking human cognitive functions for disease assessment.

Purpose of the Study:

To propose an Optimal Multi-Attention Fusion Convolutional Neural Network-based Skin Cancer Diagnosis (MAFCNN-SCD) technique.
To accurately detect and classify skin cancer using dermoscopic images.
To enhance the diagnostic capabilities in dermatology through advanced AI methods.

Main Methods:

The proposed MAFCNN-SCD technique utilizes a Multi-Attention Fusion Network (MAFNet) as a feature extractor.
Hyperparameter optimization is performed using the Henry Gas Solubility Optimization (HGSO) algorithm.
A Deep Belief Network (DBN) is employed for the final detection and classification of skin cancer.
Data pre-processing is conducted as an initial step in the methodology.

Main Results:

Simulations demonstrated the superior performance of the proposed MAFCNN-SCD technique.
Comprehensive comparative analysis confirmed the effectiveness of the MAFCNN-SCD approach.
The technique achieved high accuracy in classifying skin cancer from dermoscopic images.

Conclusions:

The MAFCNN-SCD technique offers a highly effective AI-driven solution for skin cancer diagnosis.
The proposed method outperforms existing methodologies in classifying skin cancer from dermoscopic images.
AI, specifically advanced CNN architectures, holds significant promise for improving dermatological diagnostics.