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

Skin Cancer01:30

Skin Cancer

4.8K
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...
4.8K
Classification of Leukocytes01:30

Classification of Leukocytes

3.2K
Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...
3.2K

You might also read

Related Articles

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

Sort by
Same author

Deglycosylation at War: Host N-Glycoprotein Remodeling in Infection and Immunity.

Infection and drug resistance·2026
Same author

Recharacterization of EmAGA, a Potential Candidate for Novel ALL Therapeutics.

Biomolecules·2026
Same author

Bile acid metabolomics reveals distinct immunometabolic niches and enables accurate diagnosis of AQP4-IgG-seronegative NMOSD.

Frontiers in immunology·2026
Same author

Integrated metabolomics analysis identifies distinct amino acid signatures in chronic hepatitis B patients with metabolic dysfunction-associated steatotic liver disease.

Frontiers in cellular and infection microbiology·2026
Same author

Identification and characterization of a prokaryotic Mannosyl-oligosaccharide Glucosidase (MOGS) and establishment of a functional complementation assay for MOGS activity.

Biochemical and biophysical research communications·2025
Same author

Downregulation of Endo-Beta-N-Acetylglucosaminidase in <italic>Caenorhabditis elegans</italic> Improves Stress Adaptivity.

Cells, tissues, organs·2025

Related Experiment Video

Updated: Sep 28, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.0K

Deep Learning-Based Classification for Melanoma Detection Using XceptionNet.

Xinrong Lu1, Y A Firoozeh Abolhasani Zadeh2

  • 1Gannan University of Science & Technology, Ganzhou 341000, Jiangxi, China.

Journal of Healthcare Engineering
|April 1, 2022
PubMed
Summary

This study introduces an advanced AI model for diagnosing skin cancer using dermoscopy images. The improved XceptionNet model achieves higher accuracy in early skin cancer detection compared to existing methods.

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

1.8K
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

2.5K

Related Experiment Videos

Last Updated: Sep 28, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.0K
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

1.8K
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

2.5K

Area of Science:

  • Dermatology and Artificial Intelligence
  • Medical Image Analysis
  • Computational Oncology

Background:

  • Skin cancer is a prevalent global health concern, with melanoma being a significant subtype.
  • Current diagnostic methods often rely on invasive procedures.
  • Integrating advanced computational techniques like image processing offers potential for early cancer detection.

Purpose of the Study:

  • To propose an automated method for diagnosing skin cancer utilizing dermoscopy images.
  • To enhance the accuracy of early skin cancer detection through artificial intelligence.

Main Methods:

  • Development of an improved XceptionNet model incorporating swish activation function and depthwise separable convolutions.
  • Application of the model for the automatic diagnosis of skin cancer from dermoscopy images.
  • Comparative analysis against existing state-of-the-art skin cancer diagnosis solutions.

Main Results:

  • The proposed improved XceptionNet model demonstrated enhanced classification accuracy compared to the original XceptionNet and other deep learning architectures.
  • The automated system achieved superior performance in skin cancer diagnosis when compared to other contemporary methods.
  • The study highlights the efficacy of AI in improving diagnostic accuracy for skin cancer.

Conclusions:

  • The developed AI-based system offers a promising, accurate, and automated approach for skin cancer diagnosis from dermoscopy images.
  • This method has the potential to aid physicians in the early and effective healing of skin cancer.
  • Further research and implementation of such AI tools can significantly impact dermatological diagnostics and patient outcomes.