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

7.9K
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...
7.9K

You might also read

Related Articles

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

Sort by
Same author

A complex case of clinically diagnosed tuberculosis with hypercalcemia: Not missing the tree for the forest!

IDCases·2026
Same author

2D-to-3D: Predicting three-dimensional (3D) cephalometric measurements from two conventional X-ray images : From 2D to 3D with a computational tool without using computed tomography.

Journal of orofacial orthopedics = Fortschritte der Kieferorthopadie : Organ/official journal Deutsche Gesellschaft fur Kieferorthopadie·2025
Same author

Feasibility of craniofacial landmark plotting on magnetic resonance images.

Odontology·2025
Same author

Adjuvant Gemcitabine Plus Cisplatin and Chemoradiation in Patients With Gallbladder Cancer: A Randomized Clinical Trial.

JAMA oncology·2024
Same author

A systematic review of the techniques for automatic segmentation of the human upper airway using volumetric images.

Medical & biological engineering & computing·2023
Same author

Automatic Segmentation of Organs-at-Risk in Thoracic Computed Tomography Images Using Ensembled U-Net InceptionV3 Model.

Journal of computational biology : a journal of computational molecular cell biology·2023

Related Experiment Video

Updated: Oct 3, 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

7.0K

Automatic detection of osteosarcoma based on integrated features and feature selection using binary arithmetic

Priti Bansal1, Kshitiz Gehlot2, Abhishek Singhal2

  • 1Department of Information Technology, Netaji Subhas University of Technology, Dwarka, New Delhi India.

Multimedia Tools and Applications
|February 14, 2022
PubMed
Summary

This study introduces an automated system for osteosarcoma detection in bone tumors using integrated handcrafted and deep learning features. The model achieved 99.54% accuracy after feature selection, significantly improving early diagnosis.

Keywords:
Binary arithmetic optimization algorithmEfficientNet-B0Integrated featuresOsteosarcomaWhole slide imagesXception

More Related Videos

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.7K
Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.3K

Related Experiment Videos

Last Updated: Oct 3, 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

7.0K
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.7K
Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.3K

Area of Science:

  • Oncology
  • Medical Imaging Analysis
  • Artificial Intelligence

Background:

  • Osteosarcoma is a common bone cancer in children and adolescents, with manual detection being time-consuming and expertise-dependent.
  • Timely diagnosis is crucial for reducing osteosarcoma mortality rates.
  • Automated systems offer a solution to expedite medical image analysis and reduce reliance on expert interpretation.

Purpose of the Study:

  • To develop an automated detection system, the Integrated Features-Feature Selection Model for Classification (IF-FSM-C), for osteosarcoma from whole slide images (WSIs).
  • To enhance detection accuracy by fusing handcrafted (HC) and deep learning model (DLM) features.
  • To improve classification performance through optimized feature selection.

Main Methods:

  • Proposed the IF-FSM-C system integrating features from HC techniques and DLMs (EfficientNet-B0, Xception).
  • Introduced two binary variants of the Arithmetic Optimization Algorithm (AOA), BAOA-S and BAOA-V, for feature selection (FS).
  • Classified WSIs into Viable Tumor (VT), Non-viable Tumor (NVT), and Non-Tumor (NT) categories.

Main Results:

  • The integrated features from HC techniques and Xception achieved an accuracy of 96.08%.
  • Applying BAOA-S for FS boosted the overall accuracy to 99.54%.
  • Feature selection using BAOA-S reduced the feature count from 2118 to 188, enhancing model efficiency.

Conclusions:

  • The proposed IF-FSM-C system, particularly with integrated features and BAOA-S for feature selection, demonstrates high efficacy in automated osteosarcoma detection.
  • This approach offers a promising tool for accurate and efficient diagnosis of osteosarcoma from WSIs.
  • The study highlights the potential of combining traditional and deep learning methods with advanced feature selection for improved medical image analysis.