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

Classification of Bones01:18

Classification of Bones

6.5K
The bones of the human skeletal system are of varied shapes, sizes, and functions. They can be classified based on their shape and function into four major classes: long bones, short bones, flat bones, and irregular bones. Some classifications include a fifth type, the sesamoid bones, as a separate class, whereas others categorize them under short bones.
Long and Short Bones
The appendicular skeleton, particularly the upper and lower limbs, is primarily made of long and short bones. The...
6.5K
Fractures: Bone Repair01:27

Fractures: Bone Repair

3.6K
Treatment for a fracture is based on the type of break, the bone affected, and the patient's age.
Minor fractures with no bone displacement are treated by immobilizing the fractured bone using a cast or splint. However, in the case of fractures with displaced bones, the broken bones are repositioned before immobilization to ensure successful healing without deformation and loss of function. The realignment of fractured bone ends is performed through a process called reduction. If the...
3.6K
Structural Classification of Joints01:20

Structural Classification of Joints

3.9K
Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
3.9K

You might also read

Related Articles

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

Sort by
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

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

PloS one·2025
Same author

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

PloS one·2025
Same author

Leveraging potential of limpid attention transformer with dynamic tokenization for hyperspectral image classification.

PloS one·2025
Same author

Enhancing Blood Cell Diagnosis Using Hybrid Residual and Dual Block Transformer Network.

Bioengineering (Basel, Switzerland)·2025

Related Experiment Video

Updated: Sep 1, 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

6.9K

Hybrid SFNet Model for Bone Fracture Detection and Classification Using ML/DL.

Dhirendra Prasad Yadav1, Ashish Sharma1, Senthil Athithan2

  • 1Department of Computer Engineering and Applications, GLA University, Mathura 281406, Uttar Pradesh, India.

Sensors (Basel, Switzerland)
|August 12, 2022
PubMed
Summary

This study introduces a novel deep learning model, SFNet, combined with an improved Canny edge algorithm for accurate bone fracture diagnosis from X-ray images. The AI approach significantly improves diagnostic accuracy and efficiency compared to manual methods.

Keywords:
CNNX-raybone fracturecannyclassificationfusionhybrid

More Related Videos

Assessment of Bone Fracture Healing Using Micro-Computed Tomography
12:04

Assessment of Bone Fracture Healing Using Micro-Computed Tomography

Published on: December 9, 2022

1.9K
Author Spotlight: Advanced Techniques for Characterizing Tissue Mineralization in Bone Regeneration Research
07:29

Author Spotlight: Advanced Techniques for Characterizing Tissue Mineralization in Bone Regeneration Research

Published on: September 27, 2024

874

Related Experiment Videos

Last Updated: Sep 1, 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

6.9K
Assessment of Bone Fracture Healing Using Micro-Computed Tomography
12:04

Assessment of Bone Fracture Healing Using Micro-Computed Tomography

Published on: December 9, 2022

1.9K
Author Spotlight: Advanced Techniques for Characterizing Tissue Mineralization in Bone Regeneration Research
07:29

Author Spotlight: Advanced Techniques for Characterizing Tissue Mineralization in Bone Regeneration Research

Published on: September 27, 2024

874

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Orthopedics

Background:

  • Manual bone fracture diagnosis from X-rays is time-consuming and requires expert interpretation.
  • Machine learning (ML) and deep learning (DL) offer advanced solutions for medical image analysis.
  • Accurate and efficient fracture detection is crucial for timely patient treatment.

Purpose of the Study:

  • To develop a novel deep learning model for automated bone fracture diagnosis.
  • To improve the accuracy and efficiency of fracture detection in X-ray images.
  • To investigate the impact of an improved Canny edge algorithm on fracture localization and diagnosis.

Main Methods:

  • Proposed a hybrid two-scale sequential deep learning model named SFNet (Scale Fracture Network).
  • Integrated an improved Canny edge algorithm to precisely localize fracture regions.
  • Fed grayscale and Canny edge images into the SFNet for training and evaluation.
  • Compared SFNet performance against state-of-the-art deep convolutional neural network (CNN) models.

Main Results:

  • SFNet combined with the Canny edge algorithm (SFNet + Canny) achieved superior diagnostic performance.
  • Achieved an accuracy of 99.12%, F1-score of 99%, and recall of 100% for bone fracture diagnosis.
  • Demonstrated reduced computation time compared to other deep CNN models.
  • The Canny edge algorithm significantly enhanced the performance of the CNN model.

Conclusions:

  • The proposed SFNet model with the Canny edge algorithm offers a highly efficient and accurate solution for automated bone fracture diagnosis.
  • Integrating edge detection techniques improves the localization and identification of fractures in medical imaging.
  • This AI-driven approach has the potential to streamline the diagnostic workflow and improve patient outcomes.