Jove
Visualize
Contact Us

Related Concept Videos

Skin Cancer01:30

Skin Cancer

4.9K
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...
4.9K

You might also read

Related Articles

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

Sort by
Same author

HELP: A computational framework for labelling and predicting human common and context-specific essential genes.

PLoS computational biologyยท2024
See all related articles
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 Experiment Video

Updated: Oct 22, 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

Bucket of Deep Transfer Learning Features and Classification Models for Melanoma Detection.

Mario Manzo1, Simone Pellino2

  • 1Information Technology Services, University of Naples "L'Orientale", 80121 Naples, Italy.

Journal of Imaging
|August 30, 2021
PubMed
Summary
This summary is machine-generated.

This study uses deep learning and transfer learning to improve early melanoma detection from skin lesion images. The approach enhances diagnostic accuracy for this deadly skin cancer.

Keywords:
deep learningensemble classificationmelanoma detectiontransfer learning

More Related Videos

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.4K
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

1.9K

Related Experiment Videos

Last Updated: Oct 22, 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
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.4K
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

1.9K

Area of Science:

  • Dermatology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Malignant melanoma incidence is rising globally.
  • Early diagnosis is crucial for effective melanoma treatment.
  • Deep learning, particularly convolutional neural networks (CNNs), excels at image analysis.

Purpose of the Study:

  • To develop an automated method for predicting melanoma from skin lesion images.
  • To leverage pretrained deep CNN architectures for feature extraction.
  • To enhance classification accuracy using an ensemble learning framework.

Main Methods:

  • Utilized transfer learning to extract features from skin lesion images.
  • Implemented an ensemble classification model using transferred features.
  • Trained individual classifiers on balanced data subspaces and combined predictions statistically.

Main Results:

  • The proposed approach demonstrated effectiveness in predicting skin lesions.
  • Achieved competitive results compared to existing state-of-the-art methods.
  • Validated through experimental analysis on diverse skin lesion image datasets.

Conclusions:

  • The combined transfer learning and ensemble classification framework shows promise for accurate melanoma prediction.
  • This AI-driven approach can aid in the early diagnosis of malignant melanoma.
  • Further research can refine these methods for clinical application.