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

Performance investigation of 160 Gb/s inter-satellite laser communication transmission system based on OFDM and PDM transmission with advanced signal processing techniques.

Scientific reports·2026

Same author

GEC-DTSP: A GNN-RL-based Edge-Cloud Digital Twin framework for real-time traffic forecasting and adaptive signal control.

PloS one·2026

Same author

Adaptive traffic signal control using deep reinforcement learning: Toward smarter and safer urban mobility.

PloS one·2026

Same author

Spectral efficient and high data rate ring topology based UWOC system with carrier-free optical add-drop nodes.

PloS one·2026

Same author

Watt-level high-OSNR continuous wave tunable figure-8 holmium-doped fiber laser for OWC systems.

PloS one·2025

Same author

DownLink amplified loss function module for 5G air to terrestrial communication networks.

PloS one·2025

Same journal

Correction: Luca et al. Global and Regional Diagnostic Results of Progress Toward Cervical Cancer Elimination, According to the WHO Strategy: A Systematic Literature Review with Narrative Synthesis. <i>Diagnostics</i> 2026, <i>16</i>, 1224.

Diagnostics (Basel, Switzerland)·2026

Same journal

Association Between Systemic Inflammatory Response Biomarkers and Disease Activity in Systemic Lupus Erythematosus: A Multi-Center Retrospective Study.

Diagnostics (Basel, Switzerland)·2026

Same journal

Vertebrogenic Low Back Pain and Basivertebral Nerve Ablation: A Review of Mechanisms, Imaging-Driven Selection, and Clinical Outcomes.

Diagnostics (Basel, Switzerland)·2026

Same journal

Multivalvular Carcinoid Heart Disease: The Role of Echocardiography in Diagnosis and Selection for Heterotopic Bicaval Valve Implantation.

Diagnostics (Basel, Switzerland)·2026

Same journal

Data-Efficient and Explainable Multimodal Survival Prediction in NSCLC Using Deep Image Embeddings, Clinical Variables, and Gradient-Boosted Trees.

Diagnostics (Basel, Switzerland)·2026

Same journal

Anomalous Left Coronary Artery from the Pulmonary Artery: Cinematic Volume Rendering Technique for Enhanced Anatomic Visualization.

Diagnostics (Basel, Switzerland)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Aug 13, 2025

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

A Novel Multi-Task Learning Network Based on Melanoma Segmentation and Classification with Skin Lesion Images.

Fayadh Alenezi¹, Ammar Armghan¹, Kemal Polat²

¹Department of Electrical Engineering, College of Engineering, Jouf University, Sakaka 72388, Saudi Arabia.

Diagnostics (Basel, Switzerland)

|January 21, 2023

Summary

This summary is machine-generated.

This study introduces a novel multi-task learning approach for automatic melanoma detection using deep learning on dermoscopy images. The method significantly improves early detection and classification accuracy, enhancing patient survival rates.

Keywords:

deep learning melanoma classification and segmentation multi-task learning network super-resolution

More Related Videos

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

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

Related Experiment Videos

Last Updated: Aug 13, 2025

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

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

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

Area of Science:

Dermatology
Medical Imaging
Artificial Intelligence

Background:

Melanoma is a dangerous skin cancer with high mortality.
Early detection is crucial for improving survival rates.
Automatic detection systems can aid in early diagnosis.

Purpose of the Study:

To develop and evaluate a multi-task learning approach for melanoma recognition using dermoscopy images.
To enhance the accuracy and efficiency of automatic melanoma detection.

Main Methods:

Image pre-processing using max pooling, contrast, and shape filters.
Lesion segmentation utilizing a VGGNet model-based FCN Layer architecture.
Deep learning classification with pre-trained convolutional neural networks.

Main Results:

High performance in lesion segmentation (96.99% accuracy, 98.41% sensitivity).
Excellent performance in melanoma classification (97.73% accuracy, 95.67% sensitivity).
Demonstrated effectiveness of the deep learning approach on the ISIC dataset.

Conclusions:

The proposed multi-task learning approach is effective for automated melanoma detection and classification.
The system shows potential for improving early diagnosis and patient outcomes.
Deep learning models offer a promising avenue for advancing dermatological diagnostics.