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

AdaWGAN: Data Augmentation for Few-Shot HD-sEMG Gesture Recognition Using Single-Trial Data.

IEEE journal of biomedical and health informatics·2026

Same author

Safety and feasibility of rotational atherectomy in chronic total occlusion: a systematic review and meta-analysis.

Journal of cardiothoracic surgery·2026

Same author

Advancing emergency medical team classification in MENA region: a qualitative study.

Frontiers in public health·2026

Same author

Use of intravenous antibiotics for the management of suspected chorioamnionitis: insights from women's wellness and research center.

Journal of pharmaceutical policy and practice·2026

Same author

Long-Term Outcomes of Balloon Versus Surgical Aortic Valvuloplasty in Pediatric Patients with Congenital Aortic Valve Stenosis: A Meta-Analysis.

World journal for pediatric & congenital heart surgery·2026

Same author

Efficacy of inpatient pulmonary rehabilitation in elderly patients with acute exacerbation of COPD: randomised controlled trial study.

Irish journal of medical science·2026

Same journal

Occupational Exposure to Blue Light Emitted from Neonatal Phototherapy Devices Alters the Sleep Quality of NICU Nurses.

Journal of biomedical physics & engineering·2026

Same journal

Diagnosing Epilepsy using Entropy Measures and Embedding Parameters of EEG Signals.

Journal of biomedical physics & engineering·2026

Same journal

<i>In Vitro</i> Radiosensitizing Effects of Urtica Dioica Leaf Aqueous Extract on the Killing of U87MG Glioblastoma Cell Line.

Journal of biomedical physics & engineering·2026

Same journal

Bacterial Adaptation to Radiofrequency Electromagnetic Fields Based on Experiences from Ionizing Radiation.

Journal of biomedical physics & engineering·2026

Same journal

Advanced CNN Deep Learning Model for Diabetic Retinopathy Classification.

Journal of biomedical physics & engineering·2026

Same journal

Determining the Correlation and Effect of Rectal and Bladder Volume Change in Shift of Planning Target Volume and Calculating Planning Target Volume Margin with Van Herk Formula in Prostate Cancer Tomotherapy.

Journal of biomedical physics & engineering·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Aug 9, 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

Eye Melanoma Diagnosis System using Statistical Texture Feature Extraction and Soft Computing Techniques.

Ebenezer Obaloluwa Olaniyi^1,2, Temitope Emmanuel Komolafe³, Oyebade Kayode Oyedotun⁴

¹Center for Quantum Computational System, Department of Electrical and Electronics Engineering, Adeleke University, Osun State, Nigeria.

Journal of Biomedical Physics & Engineering

|February 23, 2023

Summary

This summary is machine-generated.

This study introduces advanced soft computing techniques for diagnosing eye melanoma. The proposed Gray-Level Co-occurrence Matrix (GLCM) models achieved high accuracy, improving early detection and preventing misdiagnosis.

Keywords:

Computer Diagnostic Errors Eye Melanoma Eye-Cancer Models Neural Network Physicians Radial Basis Function Texture Analysis

More Related Videos

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

Detection and Isolation of Circulating Melanoma Cells using Photoacoustic Flowmetry

Detection and Isolation of Circulating Melanoma Cells using Photoacoustic Flowmetry

Published on: November 25, 2011

Related Experiment Videos

Last Updated: Aug 9, 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

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

Detection and Isolation of Circulating Melanoma Cells using Photoacoustic Flowmetry

Detection and Isolation of Circulating Melanoma Cells using Photoacoustic Flowmetry

Published on: November 25, 2011

Area of Science:

Ophthalmology
Medical Imaging
Artificial Intelligence

Background:

Eye melanoma is a deforming condition affecting the inner eye layers.
Symptoms include dark spots on the iris, pupil changes, and vision impairment.

Purpose of the Study:

To develop an automated diagnostic system for eye melanoma.
To improve diagnostic speed and accuracy using texture analysis and soft computing.
To prevent misdiagnosis associated with manual physician examination.

Main Methods:

Utilized Gray-Level Co-occurrence Matrix (GLCM) for texture extraction.
Employed soft computing techniques, specifically backpropagation neural networks (BPNN) and radial basis functions network (RBFN).
Trained and validated models using images from an eye-cancer database.

Main Results:

The GLCM+BPNN model achieved a recognition rate of 92.31%.
The GLCM+RBFN model demonstrated a higher recognition rate of 94.70%.
Both models showed superior performance compared to existing methods.

Conclusions:

The proposed GLCM combined with soft computing models offers a highly accurate method for eye melanoma diagnosis.
These automated approaches significantly outperform previous diagnostic models.
The study highlights the potential of AI in enhancing ophthalmic diagnostics.