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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: May 10, 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

DermViT: Diagnosis-Guided Vision Transformer for Robust and Efficient Skin Lesion Classification.

Xuejun Zhang¹, Yehui Liu¹, Ganxin Ouyang²

¹School of Computer, Electronics and Information, Guangxi University, Nanning 530004, China.

Bioengineering (Basel, Switzerland)

|April 26, 2025

Summary

DermViT, a novel deep learning model, enhances skin cancer diagnosis by mimicking physician observation. It achieves superior accuracy and efficiency in classifying dermoscopic images, improving early detection rates.

Keywords:

dermoscopic image analysis skin cancer skin lesion classification transformer

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Area of Science:

Dermatology
Artificial Intelligence
Medical Imaging

Background:

Early skin cancer diagnosis is crucial for patient survival.
Current classification models struggle with lesion variability, artifacts, and lack physician-like reasoning.

Purpose of the Study:

To develop a medically-driven deep learning architecture, DermViT, for improved skin lesion classification.
To address challenges like semantic entanglement, intra-class variability, and artifactual interference in dermoscopic images.

Main Methods:

DermViT utilizes a modular design inspired by physician diagnostic processes.
Key modules include Dermoscopic Context Pyramid (DCP) for multi-scale analysis, Dermoscopic Hierarchical Attention (DHA) for focused lesion identification, and Dermoscopic Feature Gate (DFG) for artifact suppression.

Main Results:

DermViT achieved 86.12% classification accuracy on ISIC2018 and ISIC2019 datasets, a 7.8% improvement over ViT-Base.
The model demonstrated a 40% reduction in parameters compared to ViT-Base.
Visualization confirmed DermViT's ability to accurately locate lesions even with interference.

Conclusions:

DermViT offers a more logical feature extraction and decision-making process for medical diagnosis.
The architecture provides an efficient and reliable solution for dermoscopic image analysis, enhancing early skin cancer detection.