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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: May 5, 2026

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

Skin Cancer Detection Using Deep Learning Approaches.

Shafiul Haque^1,2, Faraz Ahmad³, Vineeta Singh⁴

¹Department of Nursing, College of Nursing and Health Sciences, Jazan University, Jazan, Saudi Arabia.

Cancer Biotherapy & Radiopharmaceuticals

|March 28, 2025

Summary

Deep learning (DL) methods show promise for skin cancer detection. Convolutional neural networks (CNNs) offer high accuracy, while generative adversarial networks (GANs) aid training, but dataset limitations hinder generalizability.

Keywords:

deep neural network machine learning melanoma skin lesion support vector machine

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Area of Science:

Medical Imaging
Artificial Intelligence
Dermatology

Background:

Skin cancer is a prevalent disease with increasing incidence.
Early detection of skin cancer is crucial for effective treatment.
Traditional diagnostic methods can be invasive and costly.

Purpose of the Study:

To review deep learning (DL) methods for skin lesion identification and classification.
To assess the performance of various DL models in skin cancer detection.
To identify challenges and future directions for DL in dermatology.

Main Methods:

Examination of deep learning techniques: artificial neural networks (ANNs), convolutional neural networks (CNNs), k-nearest neighbors (KNNs), and generative adversarial networks (GANs).
Analysis of feature extraction capabilities for skin lesion identification.
Review of existing datasets and their limitations.

Main Results:

Convolutional neural networks (CNNs) demonstrated the highest accuracy in visual lesion recognition.
Generative adversarial networks (GANs) proved effective for data augmentation via simulated image creation.
Limitations include insufficient skin tone variability, high computational demands, and biased lesion representation in datasets.

Conclusions:

Diverse, high-resolution datasets and unsupervised learning are essential for developing robust DL models.
Advancements in image-based computational detection can reduce invasive procedures and expand screening.
Improved DL models promise efficient, cost-effective, and precise early skin cancer detection across diverse populations.