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

Feature selection for computerized mass detection in digitized mammograms by using a genetic algorithm.

B Zheng1, Y H Chang, X H Wang

  • 1Department of Radiology, University of Pittsburgh, PA 15261, USA.

Academic Radiology
|June 22, 1999
PubMed
Summary
This summary is machine-generated.

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

Precise Measurement of the Chromoelectric Dipole Moment of the Charm Quark.

Physical review letters·2026
Same author

Precise Measurement of Matter-Antimatter Asymmetry with Entangled Hyperon-Antihyperon Pairs.

Physical review letters·2026
Same author

Observation of Λ[over ¯]p→K^{+}π^{+}π^{-}π^{0} and Λ[over ¯]p→K^{+}π^{+}π^{-}2π^{0}.

Physical review letters·2026
Same author

First Measurement of the D_{s}^{+}→K^{0}μ^{+}ν_{μ} Decay.

Physical review letters·2026
Same author

[Construction of an index system for assessment of schistosomiasis transmission risk following natural disasters].

Zhongguo xue xi chong bing fang zhi za zhi = Chinese journal of schistosomiasis control·2026
Same author

Observation of the Electromagnetic Radiative Decays of the Λ(1520) and Λ(1690) to γΣ^{0}.

Physical review letters·2026
Same journal

Homogeneity of Liver Fat Distribution Serves as a Diagnostic Marker for Metabolic Dysfunction-Associated Steatohepatitis.

Academic radiology·2026
Same journal

MRI-based Predictors and Risk Constellations of Chronic Ankle Instability After Acute Lateral Ankle Sprain: A Multicenter Study.

Academic radiology·2026
Same journal

Early Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer using a Longitudinal US-based Stack-model.

Academic radiology·2026
Same journal

Evaluating the Impact of Embolization on Outcomes in Iliopsoas Hematomas: A Multicenter Retrospective Propensity-matched Study.

Academic radiology·2026
Same journal

Comparison of Iterative Metal Artifact Reduction Presets In Ultra-high-resolution Photon-counting CT Angiography of Patients with Total Knee Endoprosthesis.

Academic radiology·2026
Same journal

Deep Learning for Opportunistic Vertebral Fracture Detection on Routine Thoraco-abdominal Computed Tomography: A Systematic Review and Hierarchical Summary Receiver Operating Characteristic Meta-analysis of Patient-level Diagnostic Test Accuracy.

Academic radiology·2026
See all related articles

A genetic algorithm (GA) efficiently optimizes feature selection for computerized mass detection in mammograms, significantly reducing computation time compared to exhaustive search methods. This approach enhances the accuracy of identifying masses in digital mammograms.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Computerized mass detection in digitized mammograms is crucial for early cancer diagnosis.
  • Feature selection is a critical step in optimizing the performance of detection algorithms.
  • Traditional exhaustive search methods for feature selection are computationally intensive.

Purpose of the Study:

  • To optimize feature selection for computerized mass detection in digitized mammograms.
  • To compare the effectiveness of a genetic algorithm (GA) against exhaustive search for feature selection.
  • To evaluate the impact of feature set size on mass detection performance.

Main Methods:

  • A Bayesian belief network (BBN) was employed for mass region classification.

Related Experiment Videos

  • Twenty features were extracted from positive and negative mass regions in mammogram databases.
  • Performance was assessed using the area under the receiver operating characteristic curve (A), comparing exhaustive and GA-based feature selection.

    • Main Results:

    • The optimal performance (A=0.876 +/- 0.008) was achieved with 11 features.
    • Performance decreased monotonically with more than 11 features.
    • The GA identified the same optimal feature set as exhaustive search but reduced computation time by a factor of 65.

    Conclusions:

    • Genetic algorithms offer an efficient and effective method for feature selection in mass detection.
    • Optimized feature selection using GA can improve the performance of computerized mammogram analysis.
    • This approach holds promise for enhancing diagnostic accuracy in breast cancer screening.