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

Benefits of building information modeling (BIM) for construction projects success in Pakistan.

Scientific reports·2026

Same author

Impact of Project Communication on Project Success (Health Projects): Mediating Role of Work Engagement.

Journal of epidemiology and global health·2026

Same author

Explainable federated transformer framework for joint leukemia classification and stage prediction.

Scientific reports·2026

Same author

Securing IoT networks: a machine learning approach for detecting unusual traffic patterns.

Scientific reports·2025

Same author

AI-driven intrusion detection and lightweight authentication framework for secure and efficient medical sensor networks.

Scientific reports·2025

Same author

Enhanced drug-drug interaction extraction from biomedical text using deep learning-based sentence representations.

Scientific reports·2025

Same journal

Risk prediction of sepsis-associated acute kidney injury: development, validation of a machine learning model with multicenter data.

BMC medical informatics and decision making·2026

Same journal

Trajectory analysis of sleep disorders and anxiety-depression in female breast cancer patients undergoing chemotherapy: based on group-based Multi-Trajectory Model and machine learning.

BMC medical informatics and decision making·2026

Same journal

Multitask learning of longitudinal circulating biomarkers and clinical outcomes: identification of optimal machine-learning and deep-learning models.

BMC medical informatics and decision making·2026

Same journal

Comparative machine learning approaches to prognosticate clinical outcomes in oral and maxillofacial space infections: a retrospective analysis.

BMC medical informatics and decision making·2026

Same journal

Development and validation of machine learning models for early diagnosis of hemophagocytic lymphohistiocytosis in pediatric Epstein-Barr virus infection.

BMC medical informatics and decision making·2026

Same journal

Clinical subphenotypes in septic patients with new-onset atrial fibrillation: validation and parsimonious classifier model development.

BMC medical informatics and decision making·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 11, 2025

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

Skin lesion segmentation using deep learning algorithm with ant colony optimization.

Nadeem Sarwar¹, Asma Irshad², Qamar H Naith³

¹Department of Computer Science, Bahria University Lahore Campus, Lahore, Pakistan. Nadeem_srwr@yahoo.com.

BMC Medical Informatics and Decision Making

|September 28, 2024

Summary

This summary is machine-generated.

This study introduces a Hybrid Residual Networks (ResUNet) model optimized with Ant Colony Optimization (ACO) for enhanced skin lesion classification. The AI model significantly improves diagnostic accuracy, offering a promising tool for clinical use.

Keywords:

Ant Colony Optimization Deep learning Hybrid ResUNet Medical imaging Skin lesion segmentation

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

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

Related Experiment Videos

Last Updated: Jun 11, 2025

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

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

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

Area of Science:

Artificial Intelligence in Medical Imaging
Computational Pathology
Machine Learning for Dermatology

Background:

Accurate skin lesion segmentation is crucial for diagnosis and surveillance.
Deep learning models offer advancements in medical image analysis.
The Hybrid ResUNet model with Ant Colony Optimization (ACO) aims to improve skin lesion diagnosis efficiency.

Purpose of the Study:

To evaluate the Hybrid ResUNet model's effectiveness in skin lesion classification.
To assess the impact of ACO on optimizing the Hybrid ResUNet model.
To bridge the gap between computational efficiency and clinical utility in AI-driven dermatology.

Main Methods:

A deep learning approach using a Hybrid ResUNet model trained on diverse skin lesion data.
Hyperparameter optimization of the Hybrid ResUNet model using Ant Colony Optimization (ACO).
Performance evaluation using accuracy, Dice coefficient, and Jaccard index, compared against ResNet and U-Net.

Main Results:

The Hybrid ResUNet model achieved high classification accuracy (95.8%), Dice coefficient (93.1%), and Jaccard index (87.5%).
Demonstrated superior performance compared to existing state-of-the-art methods.
Showcased exceptional ability in segmenting complex skin lesions, enhancing diagnostic precision.

Conclusions:

Integrating ResUNet with ACO significantly enhances skin lesion classification accuracy.
The Hybrid ResUNet model presents a viable strategy for clinical deployment of AI tools.
Future work includes exploring multi-modal imaging, alternative optimization algorithms, and clinical applicability.