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

Fractional Order Total Variation Low-Rank Representation on Single-Cell RNA Sequencing Clustering.

IET systems biology·2026

Same author

Towards a cybersecure and privacy enhanced smart grid: A blockchain enabled federated learning framework.

PloS one·2026

Same author

Obfuscated Malware Detection and Classification in Network Traffic Leveraging Hybrid Large Language Models and Synthetic Data.

Sensors (Basel, Switzerland)·2025

Same author

FastTENET: an accelerated TENET algorithm based on manycore computing in Python.

Bioinformatics (Oxford, England)·2024

Same author

Enhanced Network Intrusion Detection System for Internet of Things Security Using Multimodal Big Data Representation with Transfer Learning and Game Theory.

Sensors (Basel, Switzerland)·2024

Same author

Intelligent decision making for energy efficient fog nodes selection and smart switching in the IOT: a machine learning approach.

PeerJ. Computer science·2024

Search research articles

Related Experiment Video

Updated: Aug 9, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

A Novel Framework for Melanoma Lesion Segmentation Using Multiparallel Depthwise Separable and Dilated Convolutions

Maryam Bukhari¹, Sadaf Yasmin¹, Adnan Habib²

¹Department of Computer Science, COMSATS University Islamabad, Attock Campus, Attock, Pakistan.

Journal of Healthcare Engineering

|February 16, 2023

Summary

This study introduces an improved skin cancer segmentation model using depthwise separable convolutions. The new method enhances melanoma lesion segmentation accuracy, crucial for early diagnosis and improved survival rates.

More Related Videos

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

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Published on: December 15, 2023

Related Experiment Videos

Last Updated: Aug 9, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

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

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Published on: December 15, 2023

Area of Science:

Dermatology and Medical Imaging
Artificial Intelligence in Healthcare

Background:

Skin cancer, particularly melanoma, has a low survival rate, making early diagnosis critical.
Accurate segmentation of melanoma lesions is challenging due to visual similarities and intraclass variations.
Existing traditional segmentation algorithms often lack automation and human input, limiting their clinical application.

Purpose of the Study:

To develop an improved, automated segmentation model for melanoma lesions.
To address the limitations of existing methods in accurately segmenting visually similar and varied lesions.

Main Methods:

Proposed an improved segmentation model utilizing depthwise separable convolutions.
Implemented parallel multidilated filters to enhance feature encoding and filter receptive fields.
Evaluated the model on three diverse datasets: DermIS, DermQuest, and ISIC2016.

Main Results:

Achieved a high Dice score of 97% for the DermIS and DermQuest datasets.
Obtained a Dice score of 94.7% on the ISIC2016 dataset.
Demonstrated superior performance in segmenting melanoma lesions compared to existing approaches.

Conclusions:

The proposed depthwise separable convolution-based model significantly improves melanoma lesion segmentation accuracy.
This automated approach offers a promising tool for early and accurate diagnosis of skin cancer.
The model's effectiveness across multiple datasets suggests its potential for broad clinical utility.