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

Development and Preliminary Validation of a Tool to Assess Barriers and Facilitators to Participation in Adjuvant Endocrine Therapy Switch Trials.

Current oncology (Toronto, Ont.)·2026

Same author

Mucosal melanoma of the head and neck region: clinical-pathologic implications of tumor microenvironment evaluated with multiplex immunohistochemistry.

Virchows Archiv : an international journal of pathology·2026

Same author

Work Ability in the Digital Age: The Role of Work Engagement, Job Resources and Traditional and Emerging Job Demands Among Older White-Collar Workers.

Behavioral sciences (Basel, Switzerland)·2026

Same author

Mutation rates in main tumour driver genes predict prognosis in patients with superficial spreading or nodular primary melanoma: results from the CARAMEL study by the Italian Melanoma Intergroup (IMI).

Experimental hematology & oncology·2026

Same author

Fatigue and adherence can challenge the prevailing wisdom on the response to severe epidemic outbreaks.

Journal of the Royal Society, Interface·2026

Same author

Non-Coding RNA Profile in the Progression of Carotid Atherosclerosis: A Systematic Review.

International journal of molecular sciences·2026

Search research articles

Related Experiment Video

Updated: Jul 10, 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

Artificial Intelligence Algorithms for Benign vs. Malignant Dermoscopic Skin Lesion Image Classification.

Francesca Brutti¹, Federica La Rosa¹, Linda Lazzeri²

¹Institute of Clinical Physiology, National Research Council, 56124 Pisa, Italy.

Bioengineering (Basel, Switzerland)

|November 25, 2023

Summary

Deep learning models significantly improve early melanoma detection compared to traditional machine learning. Convolutional neural networks offer superior accuracy and specificity in classifying skin lesions from dermoscopic images.

Keywords:

Artificial Intelligence deep learning dermoscopic images machine learning melanoma

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

Dermoscopy Aids in the Diagnosis of Discoid Lupus Erythematosus

Dermoscopy Aids in the Diagnosis of Discoid Lupus Erythematosus

Published on: May 16, 2025

Related Experiment Videos

Last Updated: Jul 10, 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

Dermoscopy Aids in the Diagnosis of Discoid Lupus Erythematosus

Dermoscopy Aids in the Diagnosis of Discoid Lupus Erythematosus

Published on: May 16, 2025

Area of Science:

Dermatology
Artificial Intelligence
Medical Imaging

Background:

Melanoma incidence is rising, necessitating improved diagnostic tools.
Early melanoma detection is critical for better patient outcomes.
Automated classification of skin lesions aids in diagnosis.

Purpose of the Study:

To compare a deep learning model with a classical machine learning model for classifying skin lesions.
To evaluate the performance of these models in distinguishing benign from malignant dermoscopic images.
To assess the generalization ability of each model.

Main Methods:

Utilized a dataset of 25,122 public dermoscopic images for training.
Employed a convolutional neural network (deep learning) and an ensemble boosted tree classifier (machine learning).
Evaluated models on a separate test set of 200 images.

Main Results:

Deep learning model achieved 85.4% accuracy and 75.5% specificity.
Machine learning model achieved 73.8% accuracy and 44.5% specificity.
The convolutional neural network demonstrated superior performance and generalization ability.

Conclusions:

Deep learning approaches, specifically convolutional neural networks, outperform traditional machine learning for melanoma detection.
Integrating advanced algorithms with dermoscopy can enhance population screening and patient management.
Improved diagnostic tools can lead to better survival rates for melanoma patients.