Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Pigmentation01:19

Pigmentation

2.5K
The color of the skin is influenced by a number of pigments, including melanin, carotene, and hemoglobin. Recall that melanin is produced by cells called melanocytes, which are found scattered throughout the stratum basale of the epidermis. The melanin is transferred to the keratinocytes via melanosomes.
Melanin occurs in two primary forms: eumelanin that provides black and brown pigment and pheomelanin that provides red color. Dark-skinned individuals produce more melanin than those with pale...
2.5K
Skin Cancer01:30

Skin Cancer

4.4K
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...
4.4K

You might also read

Related Articles

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

Sort by
Same author

The implications of FASN in viral infection and related diseases: a promising target in antiviral therapies.

Frontiers in cellular and infection microbiology·2026
Same author

Robot-assisted jaw-in-a-day based on the sandwich approach: case series.

Maxillofacial plastic and reconstructive surgery·2026
Same author

Merging evans syndrome with mucopolysaccharidosis type II: a case report.

Frontiers in pediatrics·2026
Same author

Post-Marketing Active Surveillance of Adverse Events Following Quadrivalent Subunit Influenza Vaccine in Healthy Participants Aged ≥ 3 Years in China.

Infectious diseases and therapy·2026
Same author

Sensitivity and Specificity of Reflectance Confocal Microscopy and Dermoscopy in Assessing Vitiligo Staging.

Journal of cosmetic dermatology·2026
Same author

L-2-aminoadipic acid inhibits porcine epidemic diarrhea virus replication by targeting and reducing cellular autophagy.

Veterinary microbiology·2026

Related Experiment Video

Updated: Aug 22, 2025

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

43.0K

Detection algorithm for pigmented skin disease based on classifier-level and feature-level fusion.

Li Wan1,2, Zhuang Ai3, Jinbo Chen1

  • 1Dermatology Department, Wuhan No.1 Hospital, Hubei, China.

Frontiers in Public Health
|November 7, 2022
PubMed
Summary
This summary is machine-generated.

A new fusion network algorithm accurately detects pigmented skin disease, improving upon traditional methods. This computer-aided system aids dermatologists in diagnosing skin lesions, especially in underserved areas.

Keywords:
attention mechanismfusion networkimage style transfermodel interpretabilitypigmented skin disease

More Related Videos

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.6K
Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition
09:37

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

Published on: August 18, 2022

2.4K

Related Experiment Videos

Last Updated: Aug 22, 2025

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

43.0K
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.6K
Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition
09:37

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

Published on: August 18, 2022

2.4K

Area of Science:

  • Dermatology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Pigmented skin diseases result from abnormal melanocyte and melanin production, posing a common diagnostic challenge.
  • Accurate diagnosis is crucial for reducing mortality, but expert shortages limit dermatoscopic assessments.
  • Computer-aided systems offer a solution for remote skin lesion diagnosis.

Purpose of the Study:

  • To propose and evaluate a novel fusion network algorithm for the detection of pigmented skin disease.
  • To compare different fusion strategies for optimizing diagnostic performance.
  • To validate the system's effectiveness using visualization techniques.

Main Methods:

  • Image preprocessing and data augmentation (flipping, style transfer) were employed.
  • A fusion network was developed, comparing feature-level and classifier-level fusion schemes.
  • Gradient-weighted Class Activation Mapping (Grad-CAM and Grad-CAM++) were used for model interpretability.

Main Results:

  • The proposed fusion network achieved an accuracy of 92.1% and an Area Under Curve (AUC) of 95.3%.
  • Significant improvements in evaluation indices were observed compared to traditional algorithms.
  • Visualization confirmed the effectiveness of the fusion network in identifying pigmented skin lesions.

Conclusions:

  • The developed algorithm demonstrates high accuracy and adaptability for pigmented skin disease detection.
  • This computer-aided system can effectively assist clinicians in screening and diagnosing skin conditions.
  • The method is suitable for real-world applications, particularly in areas with limited dermatological expertise.