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 author

Mm-Wave CMOS Biosensor With Integrated Dielectrophoresis for Single-Cell Detection and Characterization.

IEEE transactions on biomedical circuits and systems·2025
Same author

A review of the progress and challenges of developing dendritic-based vaccines against hepatitis B virus (HBV).

Pathology, research and practice·2025
Same author

Pan-Cancer Analysis of Oncogenic MET Fusions Reveals Distinct Pathogenomic Subsets with Differential Sensitivity to MET-Targeted Therapy.

Cancer discovery·2025
Same author

A Neural Network-based Approach to Prediction of Preterm Birth using Non-invasive Tests.

Journal of biomedical physics & engineering·2024
Same author

The efficacy and safety of ginger (Zingiber officinale) rhizome extract in outpatients with COVID-19: A randomized double-blind placebo-control clinical trial.

Medicine·2024
Same author

Efficacy and safety of deferoxamine in moderately ill COVID-19 patients: An open label, randomized controlled trial.

Medicine·2024

Related Experiment Video

Updated: Jan 9, 2026

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

43.5K

Detection and Classification of Lesions in Mammograms using One-Stage Models.

Mohammad Amin Sakha1, Ali Ameri1

  • 1Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

Journal of Biomedical Physics & Engineering
|December 11, 2025
PubMed
Summary

The YOLO-v12 AI model significantly improves breast cancer detection in mammograms, outperforming other methods. This advancement offers promising potential for early diagnosis and enhanced screening applications.

Keywords:
Artificial IntelligenceBreast Cancer ScreeningDeep LearningLesion DetectionMammography

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

7.3K
Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules
10:26

Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules

Published on: May 19, 2023

2.4K

Related Experiment Videos

Last Updated: Jan 9, 2026

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

43.5K
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

7.3K
Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules
10:26

Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules

Published on: May 19, 2023

2.4K

Area of Science:

  • Radiology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Breast cancer is the most common cancer in women, making early detection crucial.
  • Computer-Aided Diagnosis (CAD) systems aim to improve lesion detection in mammograms.
  • Artificial Intelligence (AI) in radiology shows potential for enhancing diagnostic accuracy.

Purpose of the Study:

  • To compare object detection models for smart diagnostic systems in mammography.
  • To evaluate the You Only Look Once version 12 (YOLO-v12) architecture for automated lesion detection, localization, and malignancy assessment.
  • To benchmark YOLO-v12 against Detection Transformer (DETR) and RetinaNet for mammographic analysis.

Main Methods:

  • A comparative experimental study using retrospective data.
  • Training and testing models on the Categorized Digital Database for Low-Energy and Subtracted Contrast-Enhanced Spectral Mammography (CDD-CESM) dataset.
  • Utilizing 1,982 mammograms with 3,720 annotated lesions for model evaluation.

Main Results:

  • YOLO-v12 achieved excellent diagnostic accuracy with a mean Average Precision (mAP50) of 0.98 and Intersection Over Union (IOU) of 0.95.
  • YOLO-v12 significantly outperformed contemporary models and previous YOLO versions.
  • The model demonstrated high precision in detecting and localizing lesions and assessing their status.

Conclusions:

  • AI technologies, particularly YOLO-v12, show significant potential to assist radiologists in early breast cancer detection.
  • The findings support the implementation of YOLO-v12 in clinical mammography screening.
  • Future research should focus on real-time diagnostic systems to further improve breast cancer detection capabilities.