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

Sustainable nanomaterial production from fruit waste: green synthesis of ZnO nanoparticles for antioxidant, antimicrobial, and therapeutic applications in wound healing and cancer therapy.

Drug development and industrial pharmacy·2026

Same author

Deep neural network-based robust framework for automated skin lesion segmentation and analysis.

Digital health·2026

Same author

Hybrid lightweight vision transformers with attention mechanism for feature extraction and classification of product designs.

PloS one·2026

Same author

Descriptive Analysis of Reported Adverse Events Associated with Vitiligo Medications Using FDA Adverse Event Reporting System (FAERS) Databases 2013-2023.

Diseases (Basel, Switzerland)·2025

Same author

Exploring skin picking disorder: aetiology, treatment, and future directions.

Postepy dermatologii i alergologii·2025

Same author

Nanofiltration PVP Membrane with Gradient Cross-Linking for Efficient Polycyclic Aromatic Hydrocarbons Removal.

ACS omega·2024

Same journal

Correction: Grewal et al. Diversity and Representation in Cardiovascular Research: Evidence Gaps, Emerging Models, and Policy Implications. <i>Int. J. Environ. Res. Public Health</i> 2026, <i>23</i>, 241.

International journal of environmental research and public health·2026

Same journal

Drinking Water Quality and Health Risk Assessment in Rural Ghana: Evidence from North-East and North Gonja Districts in the Savannah Region.

International journal of environmental research and public health·2026

Same journal

Physical Activity of University Students During COVID-19 Restrictions: Evidence from Poland.

International journal of environmental research and public health·2026

Same journal

Assessment of Occupational Health and Safety Hazards in Mosquito Control Personnel in North Carolina and Virginia, USA.

International journal of environmental research and public health·2026

Same journal

Association Between Dysfunctional Parenting Practices and Suspected Gaming Disorder Among Japanese Male Junior High School Students: A Cross-Sectional Study of Parental Assessment.

International journal of environmental research and public health·2026

Same journal

A National Virtual Peer Support Group for Women Veterans Living with Breast Cancer: Lessons from the Field.

International journal of environmental research and public health·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Nov 3, 2025

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition

Published on: August 18, 2022

Skin Cancer Detection: A Review Using Deep Learning Techniques.

Mehwish Dildar¹, Shumaila Akram², Muhammad Irfan³

¹Government Associate College for Women Mari Sargodha, Sargodha 40100, Pakistan.

International Journal of Environmental Research and Public Health

|June 2, 2021

Summary

This summary is machine-generated.

Early detection of skin cancer is crucial for effective treatment. This review details deep learning techniques for identifying skin cancer symptoms, aiding timely diagnosis and improving patient outcomes.

Keywords:

deep learning deep neural network (DNN)machine learning melanoma skin lesion support vector machine (SVM)

More Related Videos

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

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

Related Experiment Videos

Last Updated: Nov 3, 2025

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition

Published on: August 18, 2022

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

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

Area of Science:

Oncology
Dermatology
Artificial Intelligence

Background:

Skin cancer is a dangerous disease caused by unrepaired DNA damage in skin cells.
Early detection significantly improves treatment outcomes and survival rates.
Rising skin cancer incidence and mortality necessitate advanced diagnostic methods.

Purpose of the Study:

To systematically review deep learning techniques for early skin cancer detection.
To analyze research on using lesion characteristics for diagnosis.
To provide an overview of current deep learning approaches in skin cancer screening.

Main Methods:

Systematic review of published research papers in reputable journals.
Analysis of studies focusing on deep learning for skin cancer diagnosis.
Evaluation of techniques utilizing lesion parameters like symmetry, color, and shape.

Main Results:

Deep learning models show promise in distinguishing benign lesions from melanoma.
Various tools, graphs, and tables are used to present findings.
Identified techniques and frameworks for enhanced skin cancer detection.

Conclusions:

Deep learning offers powerful tools for the early and accurate detection of skin cancer.
Systematic review highlights the potential of AI in improving diagnostic accuracy.
Further research and implementation of these techniques can aid clinicians in patient management.