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

You might also read

Related Articles

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

Sort by
Same authorSame journal

Toward Clinical Translation of a Bimodal Fluorescence Tool for Oral Cancer Detection and Biopsy Guidance.

Journal of biophotonics·2026
Same author

In Vivo Cervical Precancer Classification Through Multifractal Analysis of Spectral Fluctuations in Intrinsic Fluorescence Spectra.

Journal of biophotonics·2025
Same author

Smartphone-based bimodal device (SBBD) for oral precancer diagnosis and biopsy guidance in clinical settings.

Optics letters·2025
Same author

Spatially Resolved Fibre-Optic Probe for Cervical Precancer Detection Using Fluorescence Spectroscopy and PCA-ANN-Based Classification Algorithm: An In Vitro Study.

Journal of biophotonics·2024
Same author

<math></math> -symmetric KdV solutions and their algebraic extension with zero-width resonances.

Scientific reports·2024
Same author

Wavelet scattering transform and entropy features in fluorescence spectral signal analysis for cervical cancer diagnosis.

Biomedical physics & engineering express·2024

Related Experiment Video

Updated: Sep 20, 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

43.0K

Empirical Mode Decomposition and Grassmann Manifold-Based Cervical Cancer Detection.

Sidharthenee Nayak1,2, Bhaswati Singha Deo3, Mayukha Pal1

  • 1ABB Ability Innovation Center, Asea Brown Boveri Company, Hyderabad, India.

Journal of Biophotonics
|May 28, 2025
PubMed
Summary

This study introduces a novel method for early cervical cancer detection using fluorescence spectroscopy. Combining empirical mode decomposition and Grassmann manifold learning achieved 99% accuracy in identifying cervical cancer stages.

Keywords:
Grassmann manifoldcervical cancerempirical mode decompositionfluorescence spectroscopymutual informationrandom Forest

More Related Videos

Multifractal Spectrum Analysis for Assessing Pulmonary Nodule Malignancy
05:24

Multifractal Spectrum Analysis for Assessing Pulmonary Nodule Malignancy

Published on: January 10, 2025

516
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.8K

Related Experiment Videos

Last Updated: Sep 20, 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

43.0K
Multifractal Spectrum Analysis for Assessing Pulmonary Nodule Malignancy
05:24

Multifractal Spectrum Analysis for Assessing Pulmonary Nodule Malignancy

Published on: January 10, 2025

516
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.8K

Area of Science:

  • Biomedical Engineering
  • Spectroscopy
  • Machine Learning

Background:

  • Cervical cancer is a leading cause of female mortality globally.
  • Early and accurate detection is vital for effective treatment and improved patient survival.
  • Fluorescence spectroscopy offers high sensitivity for detecting cancer-associated biochemical changes.

Purpose of the Study:

  • To develop and validate a reliable method for cervical cancer detection using fluorescence spectral signals.
  • To explore the efficacy of empirical mode decomposition (EMD) and Grassmann manifold (GM) learning for spectral signal analysis.
  • To enhance diagnostic accuracy and reduce computational costs in cervical cancer screening.

Main Methods:

  • Fluorescence spectral signals were collected from 110 subjects with various cervical conditions.
  • Empirical Mode Decomposition (EMD) decomposed signals into intrinsic mode functions (IMFs) for feature extraction.
  • Grassmann manifold (GM) learning and low-rank representation were used for non-linear subspace analysis and dimensionality reduction.
  • Mutual information was applied for feature selection, followed by classification using Random Forest (RF) and other machine learning models.

Main Results:

  • The combined EMD and GM approach effectively extracted unique spectral features.
  • Feature selection using mutual information significantly reduced computational load.
  • The Random Forest (RF) classifier achieved a high five-fold cross-validation accuracy of 99% with a standard deviation of 0.02.
  • The proposed method demonstrated superior performance compared to other state-of-the-art classifiers.

Conclusions:

  • The integration of EMD and GM learning provides a robust framework for analyzing fluorescence spectral data in cervical cancer detection.
  • This approach offers a highly accurate and computationally efficient method for early diagnosis.
  • The findings suggest significant potential for fluorescence spectroscopy combined with advanced machine learning techniques in improving cervical cancer screening and management.