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 Video

Updated: May 15, 2026

Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment
13:01

Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment

Published on: June 3, 2022

Evolutionary algorithm-based classifier parameter tuning for automatic ovarian cancer tissue characterization and

U R Acharya1, M R K Mookiah1, S Vinitha Sree2

  • 1Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore.

Ultraschall in Der Medizin (Stuttgart, Germany : 1980)
|December 22, 2012
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

A novel and accurate EEG emotion classification model based on multiple attention local binary patterns.

BMC medicine·2026
Same author

A multi-level attention CNN-transformer based framework for the detection of brain tumor using regional dual-score explainability.

Scientific reports·2026
Same author

TrustNet: a lightweight network with integrated uncertainty quantification and quantitative explainable AI for ischemic stroke detection in CT images.

Scientific reports·2026
Same author

Setting benchmark for ischemic stroke treated endovascularly: A systematic review and meta-analysis.

Revue neurologique·2026
Same author

TriPat‑XFE: a triangle pattern‑based explainable feature engineering framework for EEG classification.

Neuroscience·2025
Same author

Biventricular and bia-trial strain by cardiac magnetic resonance in thalassaemia intermedia: patterns and correlates.

Clinical radiology·2025
This summary is machine-generated.

This study introduces an automated computer-aided diagnostic (CAD) system for classifying ovarian tumors. The system achieves high accuracy in distinguishing benign from malignant tumors using image processing and a neural network.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Gynecologic Oncology

Background:

  • Ovarian cancer diagnosis relies on imaging, but distinguishing benign from malignant tumors is challenging.
  • Accurate, objective diagnostic tools are needed to reduce patient anxiety and healthcare costs.
  • Current methods lack the precision required for definitive tumor classification.

Purpose of the Study:

  • To develop an automated computer-aided diagnostic (CAD) system for ovarian tumor classification.
  • To improve the accuracy and objectivity of differentiating benign and malignant ovarian tumors.
  • To reduce the need for invasive procedures and associated patient distress.

Main Methods:

  • Feature extraction using Hu's invariant moments, Gabor transform, and entropies from ultrasound images.

More Related Videos

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Related Experiment Videos

Last Updated: May 15, 2026

Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment
13:01

Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment

Published on: June 3, 2022

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

  • Training a probabilistic neural network (PNN) classifier with significant features.
  • Optimizing the PNN model parameter (σ) using a genetic algorithm (GA).

    • Main Results:

    • The system was validated on 1300 benign and 1300 malignant images.
    • Achieved high classification accuracy (99.8%), sensitivity (99.2%), and specificity (99.6%).
    • Utilized 23 statistically significant features (p < 0.0001) with an optimal σ of 0.264.

    Conclusions:

    • The proposed automated CAD system offers an objective and accurate method for ovarian tumor classification.
    • The system is fast, deployable on standard computers, and can assist physicians in diagnosis.
    • It serves as a valuable adjunct tool for confident clinical decision-making in gynecologic oncology.