Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Multi-modal deep learning for paddy health assessment: fusing leaf imagery with tabular metadata using a factorized bilinear pooling approach.

Scientific reports·2026

Same author

Millet-based rice analogues for sustainable food systems: a comprehensive review on advancements, characterization, challenges, and future prospects for enhancing food security and health.

Food & function·2026

Same author

Laser Cavitation Technology for Advanced Sustainable Liquid Food Processing: A Comprehensive Review on Equipment Design, Applications, and Challenges.

Comprehensive reviews in food science and food safety·2025

Same author

Advances in heavy metal removal from fruit juices: implications for food safety and quality control.

Critical reviews in food science and nutrition·2025

Same author

Deacidification of vegetable oils: advanced technologies, mechanistic insights, and emerging strategies.

Food chemistry·2025

Same author

Integrating Ozone Pretreatment in Green Extraction Techniques for Bioactive Compounds From Agro-Industrial Residues.

Comprehensive reviews in food science and food safety·2025

Same journal

MT-MRI for detection of renal interstitial fibrosis in renovascular disease.

Scientific reports·2026

Same journal

Detection of underground objects from GPR data using a lightweight YOLO-based approach.

Scientific reports·2026

Same journal

Early systemic inflammatory-metabolic trajectory phenotypes are associated with survival outcomes in metastatic renal cell carcinoma treated with nivolumab.

Scientific reports·2026

Same journal

Water balance components in a dry-seeded rice-wheat system: Untangling the effects of tillage and mulching practices.

Scientific reports·2026

Same journal

Topological approaches to quantum tensor train compression via ZX-calculus and SVD.

Scientific reports·2026

Same journal

determinants of flood impacts and adaptive capacity among market vendors in Walukuba-Masese, Jinja city, Uganda.

Scientific reports·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jan 16, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

An advanced skin lesion segmentation and classification framework using deep learning strategies.

J Deepa¹, P Madhavan²

¹Department of Computing Technologies, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur, 603203, Tamil Nadu, India.

Scientific Reports

|September 30, 2025

Summary

This summary is machine-generated.

This study introduces an automated deep learning model for accurate skin lesion segmentation and classification, improving early melanoma diagnosis. The novel framework enhances diagnostic accuracy for malignant and benign skin lesions.

Keywords:

Adaptive layer-based visual transformer with UNet Dilated densenet with multi-head attention mechanism Improved random parameter-based galactic swarm optimization Skin lesion segmentation and classification

More Related Videos

SCAnED - An Open-source Skin Segmentation Macro for Semi-automated Cell and Nuclei Detection in Epidermal and Dermal Skin Compartments

SCAnED - An Open-source Skin Segmentation Macro for Semi-automated Cell and Nuclei Detection in Epidermal and Dermal Skin Compartments

Published on: August 8, 2025

Related Experiment Videos

Last Updated: Jan 16, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

SCAnED - An Open-source Skin Segmentation Macro for Semi-automated Cell and Nuclei Detection in Epidermal and Dermal Skin Compartments

SCAnED - An Open-source Skin Segmentation Macro for Semi-automated Cell and Nuclei Detection in Epidermal and Dermal Skin Compartments

Published on: August 8, 2025

Area of Science:

Dermatology
Artificial Intelligence
Medical Imaging

Background:

Skin cancer, particularly melanoma, presents a significant health threat due to its rapid progression and diagnostic complexities.
Accurate analysis of malignant and benign skin lesions is crucial for timely and effective patient treatment.
Traditional diagnostic methods face challenges, especially in early-stage melanoma detection, necessitating advanced automated solutions.

Purpose of the Study:

To develop and evaluate a novel automated skin lesion segmentation and classification model for improved diagnostic accuracy.
To address the complications in early-stage melanoma diagnosis, particularly those arising from color variations within lesions.
To enhance the effectiveness of skin cancer diagnosis through deep learning techniques.

Main Methods:

Image preprocessing techniques were applied to enhance the quality of collected skin lesion images.
An Adaptive Layer-based Visual Transformer with UNet (AL-VTransUNet) model was employed for image segmentation, with parameters optimized by Improved Random Parameter-based Galactic Swarm Optimization (IRP-GSO).
A Dilated DenseNet with a Multi-Head Attention mechanism (DD-MHA) was utilized for classification, with variables tuned using the IRP-GSO algorithm.

Main Results:

The proposed AL-VTransUNet model demonstrated effective segmentation of skin lesions.
The DD-MHA classifier, optimized with IRP-GSO, achieved improved classification accuracy for skin lesions.
The framework's efficiency was validated against existing optimization and classification approaches.

Conclusions:

The developed automated framework offers a promising approach for accurate skin lesion segmentation and classification.
This deep learning model can aid in the early and precise diagnosis of skin cancer, including melanoma.
The integration of advanced deep learning architectures and optimization algorithms enhances diagnostic capabilities in dermatology.