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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: Jun 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

Deep Learning-Based Synthetic Skin Lesion Image Classification.

Saadullah Farooq Abbasi¹, Muhammad Bilal², Teesta Mukherjee¹

¹Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Birmingham B15 2TT, United Kingdom.

Studies in Health Technology and Informatics

|August 23, 2024

Summary

This summary is machine-generated.

Researchers developed a VGG16-based algorithm to detect AI-generated medical images, achieving 99.82% accuracy in distinguishing synthetic from real skin lesion images.

Keywords:

Synthetic data VGG16 convolutional neural network generative adversarial network medical images

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Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

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Area of Science:

Artificial Intelligence
Medical Imaging
Computer Vision

Background:

High-quality synthetic medical images are now indistinguishable from real ones to the human eye.
The proliferation of AI-generated medical images necessitates robust detection methods.
Generative Adversarial Networks (GANs) can produce realistic synthetic medical data.

Purpose of the Study:

To develop and evaluate an algorithm for recognizing AI-generated medical images.
To assess the efficacy of a modified VGG16 architecture for image classification.
To provide a reliable method for differentiating synthetic from authentic medical visuals.

Main Methods:

Generated 10,000 synthetic medical skin lesion images using a Generative Adversarial Network (GAN).
Developed an enhanced VGG16-based algorithm for classifying real versus AI-generated images.
Tuned hyperparameters and trained the VGG16 model for optimal performance.

Main Results:

The enhanced VGG16-based algorithm achieved a classification accuracy of 99.82%.
The model demonstrated high efficacy in distinguishing between real and AI-generated medical images.
Multiple evaluation metrics confirmed the proposed network's effectiveness.

Conclusions:

The developed VGG16-based algorithm is highly effective for detecting AI-generated medical images.
This research contributes a valuable tool for verifying the authenticity of medical imaging data.
The dataset is publicly available for further research in medical image analysis.