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 Experiment Videos

PCOSFusion: a hybrid HOG-LBP feature-based approach for PCOS classification using StackPCOS and StackBoostPCOS.

Palvi Soni1, Chirag Sharma1, Deepak Prashar1,2

  • 1Department of Computer Science and Engineering, Lovely Professional University, Phagwara, Punjab, India.

Scientific Reports
|July 4, 2026
PubMed
Summary

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Minimally Invasive Reconstruction of Traumatic Segmental Femoral Bone Loss Using a Non-Vascularised Ipsilateral Fibular Graft in a Three-Year-Old Child: A Case Report.

Cureus·2026
Same author

RUL-DBRS: A Novel Energy Efficient and Robust Protocol for Enhanced Communication in Underwater Wireless Sensor Networks.

Scientific reports·2026
Same author

Cortical Window Osteotomy for Diagnostic Biopsy in Iliac Osteomyelitis: A Case Report and Technical Note.

Journal of orthopaedic case reports·2026
Same author

Knowledge enhanced framework for managing electricity generation and consumption in micro smart grids using Heronian mean MCDM approach.

Scientific reports·2026
Same author

Probiotic-Induced Gut Microbiota Modulation: A Comparative Analysis Using 16S rRNA V3-V4 and Targeted Sequencing.

Microorganisms·2026
Same author

Optimizing hepatitis C diagnosis through reinforcement learning feature selection and multi-model machine learning evaluation.

Scientific reports·2026
Same journal

MT-MRI for detection of renal interstitial fibrosis in renovascular disease.

Scientific reports·2026
Same journal

Detection of underground objects from GPR data using a lightweight YOLO-based approach.

Scientific reports·2026
Same journal

Early systemic inflammatory-metabolic trajectory phenotypes are associated with survival outcomes in metastatic renal cell carcinoma treated with nivolumab.

Scientific reports·2026
Same journal

Water balance components in a dry-seeded rice-wheat system: Untangling the effects of tillage and mulching practices.

Scientific reports·2026
Same journal

Topological approaches to quantum tensor train compression via ZX-calculus and SVD.

Scientific reports·2026
Same journal

determinants of flood impacts and adaptive capacity among market vendors in Walukuba-Masese, Jinja city, Uganda.

Scientific reports·2026
See all related articles
This summary is machine-generated.

This study introduces an automated method for Polycystic Ovary Syndrome (PCOS) detection using image analysis and machine learning. The hybrid approach achieved high accuracy, aiding early diagnosis of this common reproductive health condition.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Women's Health

Background:

  • Polycystic Ovary Syndrome (PCOS) is a common condition impacting female reproductive health.
  • Early and accurate diagnosis of PCOS is crucial for effective management.
  • Medical image analysis offers a promising avenue for automated PCOS detection.

Purpose of the Study:

  • To develop and evaluate a hybrid automated binary classification system for PCOS detection.
  • To investigate the efficacy of stacking ensemble learning models combined with advanced feature extraction for PCOS diagnosis.
  • To compare two ensemble strategies, one with four classifiers and another with five including Gradient Boosting.

Main Methods:

  • Utilized the PCOSFusion algorithm for feature extraction from ovarian medical images.
Keywords:
Ensemble techniquesLogistic regressionMachine learningPCOSSVMVoting classifier

Related Experiment Videos

  • Employed stacking ensemble learning with multiple base classifiers.
  • Investigated two ensemble configurations: a four-classifier model and a five-classifier model incorporating Gradient Boosting.

    • Main Results:

    • Both proposed strategies effectively distinguished between PCOS and non-PCOS cases.
    • The stacking ensemble approach leveraged the combined strengths of individual classifiers.
    • The model incorporating Gradient Boosting showed a slight performance enhancement, achieving 98.44% accuracy, 99.35% precision, and 98.49% recall.

    Conclusions:

    • Stacking-based ensemble techniques, coupled with effective feature extraction, show significant potential for enhancing diagnostic accuracy in medical imaging.
    • The developed hybrid approach is a viable tool to assist medical professionals in the early detection of PCOS.
    • Automated image analysis can improve the efficiency and accuracy of PCOS diagnosis.