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

Predicting the Path to Attrition: Multidomain Risk Assessment in Diabetic Foot Ulcer Offloading Randomized Controlled Trials.

Diabetes care·2026
Same author

Structural variant calling using Sniffles2.

Nature protocols·2026
Same author

AI-Generated Exercise Prescriptions for At-Risk Populations: Safety and Feasibility of a Large Language Model Assessed by Expert Evaluation.

Journal of clinical medicine·2026
Same author

A novel interpretable classification of lumbar spinal stenosis using a cascade deep learning approach and T2-weighted MRI.

Journal of neurosurgery. Spine·2026
Same author

MosaicSim: A Novel Mosaic Variant Simulator Reveals Diminishing Returns of Ultra-High Coverage for Mosaic Variant Detection.

bioRxiv : the preprint server for biology·2026
Same author

A multi-head YOLOv12 with self-supervised pretraining for urinary sediment particle detection.

Scientific reports·2025

Related Experiment Video

Updated: Jul 10, 2025

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

6.8K

Breast Cancer Detection with an Ensemble of Deep Learning Networks Using a Consensus-Adaptive Weighting Method.

Mohammad Dehghan Rouzi1, Behzad Moshiri1,2, Mohammad Khoshnevisan3

  • 1School of Electrical and computer Engineering, College of Engineering, University of Tehran, Tehran 14174-66191, Iran.

Journal of Imaging
|November 24, 2023
PubMed
Summary

This study introduces a novel computer-aided detection (CAD) system using deep learning for earlier breast cancer diagnosis. The advanced ensemble method improves detection accuracy, aiding timely intervention and better patient outcomes.

Keywords:
breast cancercomputer-aided detectionconsensus-adaptive weightingdeep learningensemble learningmammogramsmedical image analysisradiology

More Related Videos

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.1K
Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
15:48

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

22.5K

Related Experiment Videos

Last Updated: Jul 10, 2025

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

6.8K
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.1K
Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
15:48

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

22.5K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Healthcare
  • Oncology

Background:

  • Late diagnosis significantly contributes to breast cancer mortality.
  • Mammography is crucial for early detection but has limitations.
  • Accurate and rapid diagnosis is essential for effective breast cancer treatment.

Purpose of the Study:

  • To develop and evaluate a novel computer-aided detection (CAD) ensemble system for enhanced breast cancer diagnosis.
  • To improve the accuracy and speed of early breast cancer detection using deep learning.
  • To address challenges in pixel-level annotation for deep learning models in mammography.

Main Methods:

  • An ensemble CAD system was created integrating EfficientNet, Xception, MobileNetV2, InceptionV3, and Resnet50 deep learning networks.
  • A novel consensus-adaptive weighting (CAW) method was employed for dynamic adjustment of network contributions.
  • The system was evaluated on the Digital Database for Screening Mammography (DDSM) and INbreast datasets, including a cropped DDSM subset.

Main Results:

  • The CAD system demonstrated superior performance across evaluated datasets.
  • On the cropped DDSM dataset, the system achieved a 95.48% accuracy.
  • Detection rates were enhanced by approximately 1.59% on the cropped DDSM dataset.

Conclusions:

  • The developed CAD ensemble system represents a significant advancement in early breast cancer detection.
  • The system offers potential for more precise and timely diagnosis, improving patient outcomes.
  • The consensus-adaptive weighting method effectively integrates multiple deep learning networks for robust detection.