Jove
Visualize
Contact Us
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 Concept Videos

Skin Cancer01:30

Skin Cancer

5.0K
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...
5.0K

You might also read

Related Articles

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

Sort by
Same author

Refractive Index Spectral Fingerprints of Pathogenic Bacteria Revealed by Monte Carlo-Optimized Opto-Microfluidic Extinction Spectroscopy.

Journal of biophotonics·2026
Same author

Water-Mediated Ligand Dynamics Enable Solution-Phase Self-Assembly of Perovskite Nanocrystals Into 3D Ordered Architectures.

Small (Weinheim an der Bergstrasse, Germany)·2026
Same author

Enhanced Detection of Algal Leaf Spot, Tea Brown Blight, and Tea Grey Blight Diseases Using YOLOv5 Bi-HIC Model with Instance and Context Information.

Plants (Basel, Switzerland)·2025
Same author

ColoPola: A polarimetric imaging dataset for colorectal cancer detection.

GigaScience·2025
Same author

Analysis of polarization features of human breast cancer tissue by Mueller matrix visualization.

Journal of biomedical optics·2024
Same author

Decomposition Mueller matrix polarimetry for enhanced miRNA detection with antimonene-based surface plasmon resonance sensor and DNA-linked gold nanoparticle signal amplification.

Talanta·2024

Related Experiment Video

Updated: Oct 29, 2025

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

43.1K

Characterization of Mueller matrix elements for classifying human skin cancer utilizing random forest algorithm.

Ngan Thanh Luu1,2, Thanh-Hai Le2,3, Quoc-Hung Phan4

  • 1International University, School of Biomedical Engineering, Ho Chi Minh City, Vietnam.

Journal of Biomedical Optics
|July 6, 2021
PubMed
Summary

This study introduces a new method using Mueller matrix elements and a random forest algorithm for accurate skin cancer detection. The approach effectively classifies melanoma and non-melanoma lesions with high precision.

Keywords:
Stokes–Mueller matrix formalismclassificationhuman skin cancerrandom forest

More Related Videos

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.4K
Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

580

Related Experiment Videos

Last Updated: Oct 29, 2025

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

43.1K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.4K
Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

580

Area of Science:

  • Biomedical Optics
  • Computational Biology
  • Dermatology

Background:

  • Mueller matrix decomposition is a key technique for analyzing biological samples.
  • Existing methods have limitations in accuracy due to assumptions about optical effect sequences.

Purpose of the Study:

  • To develop a novel approach for detecting and classifying human skin cancer using Mueller matrix properties.
  • To improve the accuracy and applicability of optical methods in skin cancer diagnosis.

Main Methods:

  • Utilized Mueller matrix elements from 32 tissue samples (including melanoma, SCC, BCC, and normal) as input.
  • Employed a random forest (RF) classifier with 669 data points for classification.
  • Analyzed both linear and circular polarization properties within the Mueller matrix elements.

Main Results:

  • Achieved an average classification precision of 93% for skin cancer lesions.
  • Demonstrated that circular polarization properties significantly influence classification outcomes.
  • Identified specific Mueller matrix elements (m44, m34, m24, m14) as dominant predictors.

Conclusions:

  • The proposed method offers a simple, accurate, and cost-effective solution for skin cancer classification and diagnosis.
  • Highlights the importance of circular polarization in optical skin cancer detection.
  • Provides a foundation for developing advanced diagnostic tools for dermatological conditions.