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

Logic-based Approach and Visualization for the Nuclear Medicine Rescheduling Problem.

Journal of medical systems·2025
Same author

A multi-model deep learning approach for the identification of coronary artery calcifications within 2D coronary angiography images.

International journal of computer assisted radiology and surgery·2025
Same author

Beyond rankings: Learning (more) from algorithm validation.

Medical image analysis·2023
Same author

Heidelberg colorectal data set for surgical data science in the sensor operating room.

Scientific data·2021
Same author

Comparative validation of multi-instance instrument segmentation in endoscopy: Results of the ROBUST-MIS 2019 challenge.

Medical image analysis·2021
Same author

Data reduction and data visualization for automatic diagnosis using gene expression and clinical data.

Artificial intelligence in medicine·2020

Related Experiment Video

Updated: Jan 11, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.3K

A Dual-stage Deep Learning Framework for Breast Ultrasound Image Segmentation and Classification.

Pierangela Bruno1, Megan Macrì2, Carmine Dodaro2

  • 1Department of Mathematics and Computer Science, University of Calabria, Rende, 87036, CS, Italy. pierangela.bruno@unical.it.

Journal of Medical Systems
|November 17, 2025
PubMed
Summary
This summary is machine-generated.

Deep learning models can segment and classify breast masses in ultrasound images, improving early breast cancer detection. This AI approach enhances diagnostic accuracy for malignant versus benign tumors.

Keywords:
Breast cancerClassificationDeep learningSegmentation

More Related Videos

Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

10.5K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

727

Related Experiment Videos

Last Updated: Jan 11, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.3K
Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

10.5K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

727

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Breast cancer is a leading cause of death among women, making early detection critical.
  • Deep Learning (DL) shows promise in enhancing medical image analysis for diagnostics.
  • Ultrasound imaging is a key tool for breast mass evaluation.

Purpose of the Study:

  • To apply Deep Learning techniques for segmenting and classifying breast masses in ultrasound images.
  • To develop a dual-stage pipeline for improved breast cancer diagnosis.
  • To evaluate the performance of different DL architectures for this task.

Main Methods:

  • A modular, dual-stage DL pipeline was proposed: segmentation followed by classification.
  • The pipeline flexibly integrates various backbone architectures (e.g., ResNet34, MobileNetV3-Small, EfficientNet-B0).
  • An ablation study was performed to optimize model parameters.

Main Results:

  • DeepLabV3+ with ResNet34 achieved the most accurate segmentation of suspicious regions.
  • Lightweight classifiers MobileNetV3-Small and EfficientNet-B0 demonstrated superior classification performance.
  • The approach showed promising improvements in diagnostic accuracy on two breast ultrasound datasets.

Conclusions:

  • The proposed DL pipeline effectively segments and classifies breast masses using ultrasound images.
  • The method has the potential to significantly enhance early breast cancer detection and diagnostic accuracy.
  • Flexible integration of DL architectures allows for task-specific optimization.