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

Association Areas of the Cortex01:21

Association Areas of the Cortex

10.0K
Association areas are regions of the cerebral cortex that do not have a specific sensory or motor function. Instead, they integrate and interpret information from various sources to enable higher cognitive processes such as memory, learning, and decision-making. Some key association areas include the following:
Prefrontal Association Area: This area is located in the frontal lobe and is involved in planning, decision-making, and moderating social behavior. It connects with primary motor areas,...
10.0K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

FAA-Net: Fetal abdominal anomaly diagnosis in prenatal ultrasound via LLM-enhanced multi-instance learning.

Medical image analysis·2026
Same author

Identification of preeclampsia-associated immune-related risk loci in the Chinese population.

Taiwanese journal of obstetrics & gynecology·2026
Same author

Artificial intelligence for detecting fetal orofacial clefts and advancing medical education.

Nature communications·2026
Same author

Multi-view Chest X-Ray Vision-Language Pre-training via Semantic-Aware Masked Language Modeling and High-order Alignment.

IEEE transactions on medical imaging·2026
Same author

Diffusion models for brain imaging computing: a survey of frameworks and applications.

Brain informatics·2026
Same author

Multimodal artificial intelligence in retinopathy of prematurity: A comprehensive narrative review.

Survey of ophthalmology·2026

Related Experiment Video

Updated: Mar 2, 2026

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

10.1K

A Deep Convolutional Neural Network-Based Framework for Automatic Fetal Facial Standard Plane Recognition.

Zhen Yu, Ee-Leng Tan, Dong Ni

    IEEE Journal of Biomedical and Health Informatics
    |May 24, 2017
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a deep convolutional neural network (DCNN) to automatically identify fetal facial standard planes (FFSP) in prenatal ultrasound images. The DCNN method significantly improves recognition accuracy compared to traditional approaches.

    More Related Videos

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

    1.1K
    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

    3.6K

    Related Experiment Videos

    Last Updated: Mar 2, 2026

    Deep Neural Networks for Image-Based Dietary Assessment
    13:19

    Deep Neural Networks for Image-Based Dietary Assessment

    Published on: March 13, 2021

    10.1K
    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

    1.1K
    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

    3.6K

    Area of Science:

    • Medical Imaging
    • Artificial Intelligence
    • Obstetrics

    Background:

    • Prenatal diagnosis relies heavily on ultrasound imaging.
    • Accurate identification of fetal facial standard planes (FFSP) is crucial for diagnosis and measurement.
    • Existing FFSP recognition methods struggle with variations and visual similarities, yielding unsatisfactory performance.

    Purpose of the Study:

    • To develop an automated method for recognizing FFSP using a deep convolutional neural network (DCNN).
    • To enhance the accuracy and reliability of FFSP identification in prenatal ultrasound scans.

    Main Methods:

    • A deep convolutional neural network (DCNN) architecture with 16 convolutional layers and small kernels was designed.
    • Global average pooling was employed to reduce network parameters and mitigate overfitting.
    • Transfer learning and tailored data augmentation techniques were utilized to improve performance with limited data.

    Main Results:

    • The proposed DCNN method demonstrated superior performance in FFSP recognition compared to traditional approaches.
    • The DCNN effectively addressed challenges posed by intraclass variation and visual similarity.
    • The method showed effectiveness for clinical diagnosis applications.

    Conclusions:

    • Deep convolutional neural networks offer a powerful tool for automated FFSP recognition in prenatal ultrasound.
    • The developed DCNN method enhances diagnostic accuracy and efficiency in fetal imaging.
    • This approach holds significant potential for improving prenatal diagnostic capabilities.