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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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Updated: Dec 18, 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

A GAN-based image synthesis method for skin lesion classification.

Zhiwei Qin¹, Zhao Liu², Ping Zhu¹

¹The State key laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.

Computer Methods and Programs in Biomedicine

|June 12, 2020

Summary

This summary is machine-generated.

Generative adversarial networks (GANs) create realistic skin lesion images to improve melanoma classification accuracy. This data augmentation technique enhances diagnostic tools for dermatologists, aiding in early cancer detection.

Keywords:

Data augmentation Generative adversarial networks Image synthesis Skin lesion classification Transfer learning

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Area of Science:

Medical Imaging
Artificial Intelligence
Dermatology

Background:

Melanoma is the deadliest form of skin cancer, necessitating accurate diagnostic tools.
Dermoscopy is crucial for screening skin lesions, but classification is challenging due to limited and imbalanced datasets.
Computer-aided diagnostic techniques require robust data augmentation, particularly using generative adversarial networks (GANs).

Purpose of the Study:

To develop a novel GAN-based data augmentation technique for skin lesion classification.
To improve the accuracy and efficiency of melanoma detection through enhanced diagnostic models.
To provide dermatologists with more reliable tools for accurate diagnostic decisions.

Main Methods:

Proposed a skin lesion style-based generative adversarial network (GAN) by modifying the style control and noise input of the original generator.
Developed a classifier using a pre-trained deep neural network and transfer learning.
Integrated synthetically generated skin lesion images into the training dataset to enhance classifier performance.

Main Results:

The proposed skin lesion style-based GAN demonstrated superior performance in quantitative evaluations (Inception Score, FID, Precision, Recall) compared to other GAN models.
Incorporating synthesized images improved key classification metrics: accuracy (95.2%), sensitivity (83.2%), specificity (74.3%), average precision (96.6%), and balanced multiclass accuracy (83.1%) on the ISIC 2018 dataset.
Performance gains included a 1.6% increase in accuracy, 24.4% in sensitivity, 3.6% in specificity, 23.2% in average precision, and 5.6% in balanced multiclass accuracy compared to a CNN model without augmentation.

Conclusions:

The developed skin lesion style-based GANs efficiently generate high-quality medical images.
This data augmentation approach significantly improves the performance of skin lesion classification models.
The study offers a valuable deep learning-based reference for medical image analysis in dermatology.