Jove
Visualize
Contact Us

Related Concept Videos

Skin Cancer01:30

Skin Cancer

2.9K
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...
2.9K

You might also read

Related Articles

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

Sort by
Same author

Balancing Between Privacy and Utility for Affect Recognition Using Multitask Learning in Differential Privacy-Added Federated Learning Settings: Quantitative Study.

JMIR mental health·2024
Same author

Food Tray Sealing Fault Detection in Multi-Spectral Images Using Data Fusion and Deep Learning Techniques.

Journal of imaging·2021
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: May 9, 2025

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

2.2K

Melanoma Skin Classification Using the Hybrid Approach Residual Network-Vision Transformer for Cancer Diagnosis.

Alousseyni Toure1, Ismael Adji Haman1, Samir Benbakreti1

  • 1Department of Specialty, National High School of Telecommunication and ICT, Oran, Algeria.

Journal of Clinical Ultrasound : JCU
|May 2, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a hybrid ResNet50-Vision Transformer model for improved skin melanoma classification. This deep learning approach achieved a 95.53% accuracy rate, offering a valuable tool for early cancer detection.

Keywords:
ResNet50deep learningmedical image analysismelanoma skin cancervision transformer

More Related Videos

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

1.7K
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

6.6K

Related Experiment Videos

Last Updated: May 9, 2025

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

2.2K
A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

1.7K
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

6.6K

Area of Science:

  • Artificial Intelligence
  • Medical Imaging
  • Computational Biology

Background:

  • Deep neural networks (DNNs) enable advanced image and video analysis for diagnosing various pathologies, aiding physicians in conditions like skin cancer.
  • Computer-aided diagnosis (CAD) systems leverage DNNs to process medical data, providing crucial insights for disease screening and diagnosis.

Purpose of the Study:

  • To evaluate the effectiveness of Convolutional Neural Networks (CNNs), pre-trained models, and Vision Transformer (ViT) architectures for skin melanoma classification.
  • To develop and assess a hybrid deep learning architecture for enhanced melanoma diagnosis accuracy.

Main Methods:

  • The study explored classical CNNs, including Inception V3, ResNet 50, AlexNet, and EfficientNet, alongside Vision Transformer models.
  • A hybrid architecture was developed by combining the pre-trained ResNet50 model with the Vision Transformer (ViT).
  • Hyperparameter tuning was performed to optimize the performance of the developed deep learning models.

Main Results:

  • The hybrid ResNet50-ViT architecture demonstrated superior performance compared to individual models.
  • The combined model leveraged the strengths of both ResNet50 and ViT, leading to improved classification accuracy.
  • The final hybrid ResNet50-ViT model achieved an outstanding classification rate of 95.53% on the dataset.

Conclusions:

  • The developed hybrid ResNet50-ViT framework offers a powerful and accurate tool for melanoma diagnosis.
  • This research aims to support clinicians by providing a robust AI-driven solution for skin cancer screening.
  • The findings highlight the potential of hybrid deep learning models in advancing medical diagnostic capabilities.