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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: Apr 10, 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

Precision and efficiency in skin cancer segmentation through a dual encoder deep learning model.

Asaad Ahmed¹, Guangmin Sun¹, Anas Bilal²

¹School of Information Science and Technology, Beijing University of Technology, Beijing, 100124, China.

Scientific Reports

|February 9, 2025

Summary

This study introduces Dual Skin Segmentation (DuaSkinSeg), a deep learning model for precise skin lesion segmentation. DuaSkinSeg enhances diagnostic accuracy by combining efficient local and robust long-range feature extraction for better skin cancer detection.

Keywords:

Convolutional Neural Networks (CNNs)Dual Encoder Skin Lesion Segmentation ViT-CNN Vision Transformer (ViT)

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Area of Science:

Dermatology
Medical Imaging
Computer Vision
Artificial Intelligence

Background:

Skin cancer is a significant global health issue.
Accurate segmentation of skin lesions is vital for early diagnosis and effective treatment planning.
Current segmentation methods often struggle to balance computational efficiency with comprehensive feature extraction.

Purpose of the Study:

To introduce DuaSkinSeg, a novel deep learning model designed for improved skin lesion segmentation.
To address the limitations of existing methods by enhancing both efficiency and feature extraction capabilities.
To leverage a dual-encoder architecture for superior performance in segmenting skin lesions.

Main Methods:

Developed DuaSkinSeg, a deep learning model featuring a dual-encoder architecture.
Utilized a pre-trained MobileNetV2 for efficient local feature extraction.
Employed a Vision Transformer-Convolutional Neural Network (ViT-CNN) encoder-decoder for high-level feature extraction and long-range dependency analysis.

Main Results:

DuaSkinSeg demonstrated competitive performance across three benchmark datasets (ISIC 2016, 2017, 2018).
The dual-encoder approach effectively combined local and global feature extraction for enhanced segmentation accuracy.
Achieved comparable or superior results to existing state-of-the-art methods in skin lesion segmentation.

Conclusions:

The proposed DuaSkinSeg model offers a promising solution for accurate and efficient skin lesion segmentation.
The dual-encoder strategy represents a viable approach for improving deep learning models in medical image analysis.
DuaSkinSeg has the potential to aid in the early and accurate diagnosis of skin cancer.