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

Knee Joint01:23

Knee Joint

3.4K
The knee joint is the most complicated joint in the body. It consists of three articulations– two tibiofemoral and one patellofemoral. As is characteristic of synovial joints, the knee joint has a thin articular capsule that partially surrounds this joint cavity. Additionally, several ligaments, muscles, and cartilaginous structures support the movement of the knee.
A total of seven ligaments support the knee joint. The patellar ligament, which is also attached to the quadriceps femoris...
3.4K

You might also read

Related Articles

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

Sort by
Same author

Enhanced detection of network intrusions and anomalies in internet of things applications using a hybrid artificial intelligence model combining CNN and LSTM.

Scientific reports·2026
Same author

Explainable Lightweight Model Using Low-Rank and Convolutional Block Attention for Pancreatic Cancer Diagnosis.

The international journal of medical robotics + computer assisted surgery : MRCAS·2026
Same author

Gastrointestinal Lesion Detection Using Ensemble Deep Learning Through Global Contextual Information.

Bioengineering (Basel, Switzerland)·2025
Same author

Hybrid lightweight transformer for efficient landslide change detection in remote sensing imagery.

Scientific reports·2025
Same author

Diagnosis of colorectal cancer using residual transformer with mixed attention and explainable AI.

PloS one·2025
Same author

A non-sub-sampled shearlet transform-based deep learning sub band enhancement and fusion method for multi-modal images.

Scientific reports·2025

Related Experiment Video

Updated: May 2, 2026

A Novel Application of Musculoskeletal Ultrasound Imaging
10:53

A Novel Application of Musculoskeletal Ultrasound Imaging

Published on: September 17, 2013

24.3K

MV2SwimNet: A lightweight transformer-based hybrid model for knee meniscus tears detection.

Vishesh Tanwar1, Bhisham Sharma2, Dhirendra Prasad Yadav3

  • 1Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India.

Plos One
|August 27, 2025
PubMed
Summary

MV2SwimNet, a novel deep learning model combining MobileNetV2 and Swin Transformer, significantly improves knee MRI analysis for meniscus injuries. This automated tool achieves high accuracy, offering a robust alternative to manual diagnosis.

More Related Videos

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

2.0K
In Vitro Application of a Wireless Sensor in Flexion-Extension Gap Balance of Unicompartmental Knee Arthroplasty
07:33

In Vitro Application of a Wireless Sensor in Flexion-Extension Gap Balance of Unicompartmental Knee Arthroplasty

Published on: May 5, 2023

722

Related Experiment Videos

Last Updated: May 2, 2026

A Novel Application of Musculoskeletal Ultrasound Imaging
10:53

A Novel Application of Musculoskeletal Ultrasound Imaging

Published on: September 17, 2013

24.3K
A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

2.0K
In Vitro Application of a Wireless Sensor in Flexion-Extension Gap Balance of Unicompartmental Knee Arthroplasty
07:33

In Vitro Application of a Wireless Sensor in Flexion-Extension Gap Balance of Unicompartmental Knee Arthroplasty

Published on: May 5, 2023

722

Area of Science:

  • Medical Imaging Analysis
  • Artificial Intelligence in Healthcare
  • Orthopedic Diagnostics

Background:

  • Knee ailments, particularly meniscus injuries, affect millions globally, with over 14% of individuals over 40 experiencing related conditions.
  • Conventional MRI interpretation is labor-intensive, prone to errors, and relies heavily on radiologist expertise, necessitating automated solutions.
  • Existing deep learning methods using Convolutional Neural Networks (CNNs) struggle with long-range dependencies and global context in medical images.

Purpose of the Study:

  • To develop an automated, highly accurate diagnostic tool for knee disease detection using medical imaging.
  • To overcome the limitations of CNNs in capturing global contextual information in MRI scans.
  • To introduce MV2SwimNet, a hybrid deep learning model designed for enhanced knee MRI analysis.

Main Methods:

  • Proposed MV2SwimNet, a hybrid architecture integrating MobileNetV2 and Swin Transformer.
  • Incorporated Window Multi-Head Self-Attention (W-MSA) for effective region attention in MRI scans.
  • Utilized Multi-Stage Hierarchical Representation (MSHR) for progressive and robust feature learning across different representation levels.

Main Results:

  • Achieved 99.94% accuracy on dataset1 and 96.04% accuracy on dataset2 using 3-fold cross-validation.
  • Demonstrated superior performance compared to state-of-the-art techniques in knee MRI analysis.
  • Validated the model's efficiency, robustness, and potential for real-world medical applications.

Conclusions:

  • MV2SwimNet offers a highly accurate and automated solution for knee disease detection from MRI scans.
  • The hybrid approach effectively integrates local and global features, enhancing diagnostic capabilities.
  • The model shows significant potential for clinical application, improving the efficiency and accuracy of orthopedic diagnostics.