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

Convolution Properties II01:17

Convolution Properties II

583
The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
583
Protein Networks02:26

Protein Networks

4.5K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.5K
Convolution Properties I01:20

Convolution Properties I

564
Convolution computations can be simplified by utilizing their inherent properties.
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:
564
Lung Capacity01:47

Lung Capacity

56.2K
The air in the lungs is measured in volumes and capacities. Lung volume measures reflect the amount of air taken in, released, or left over after a lung function, like a single inhalation. Lung capacity measures are sums of two or more lung volume measures.
56.2K
Predicting Molecular Geometry02:27

Predicting Molecular Geometry

45.5K
VSEPR Theory for Determination of Electron Pair Geometries
45.5K
Network Covalent Solids02:18

Network Covalent Solids

16.1K
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
16.1K

You might also read

Related Articles

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

Sort by
Same author

COX-2 inhibition improves immune system homeostasis and decreases liver damage in septic rats.

The Journal of surgical research·2009
Same author

Mass spectral characterization of organophosphate-labeled, tyrosine-containing peptides: characteristic mass fragments and a new binding motif for organophosphates.

Journal of chromatography. B, Analytical technologies in the biomedical and life sciences·2009
Same author

3D-SURFER: software for high-throughput protein surface comparison and analysis.

Bioinformatics (Oxford, England)·2009
Same author

Total arch replacement with stented elephant trunk technique: a proposed treatment for complicated Stanford type B aortic dissection.

Journal of cardiac surgery·2009
Same author

Top-emitting white organic light-emitting devices with a one-dimensional metallic-dielectric photonic crystal anode.

Optics letters·2009
Same author

[Detection of tick and tick-borne pathogen in some ports of Inner Mongolia].

Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi·2009

Related Experiment Video

Updated: Jan 22, 2026

Multifractal Spectrum Analysis for Assessing Pulmonary Nodule Malignancy
05:24

Multifractal Spectrum Analysis for Assessing Pulmonary Nodule Malignancy

Published on: January 10, 2025

771

Predicting lung nodule malignancies by combining deep convolutional neural network and handcrafted features.

Shulong Li1, Panpan Xu2, Bin Li1

  • 1Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, People's Republic of China.

Physics in Medicine and Biology
|July 16, 2019
PubMed
Summary

This study introduces a novel fusion algorithm combining handcrafted and deep learning features for accurate lung nodule malignancy prediction in low-dose CT scans. The approach enhances diagnostic performance for lung cancer screening.

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

1.0K
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.9K

Related Experiment Videos

Last Updated: Jan 22, 2026

Multifractal Spectrum Analysis for Assessing Pulmonary Nodule Malignancy
05:24

Multifractal Spectrum Analysis for Assessing Pulmonary Nodule Malignancy

Published on: January 10, 2025

771
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

1.0K
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.9K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Lung cancer screening using low-dose CT (LDCT) requires high accuracy in predicting nodule malignancy.
  • Current methods may struggle with sensitivity and specificity, necessitating improved diagnostic tools.

Purpose of the Study:

  • To develop and evaluate a fusion algorithm for enhanced lung nodule malignancy prediction in LDCT scans.
  • To combine handcrafted features (HF) with deep convolutional neural network (CNN) features for improved classification performance.

Main Methods:

  • Extracted 29 handcrafted features (intensity, geometric, texture) and CNN features from 3D CNN architectures (AlexNet, VGG-16 Net, Multi-crop Net).
  • Integrated HF with CNN features as input for a support vector machine (SVM) classifier, utilizing sequential forward feature selection (SFS).
  • Validated the fusion algorithm on a dataset of 431 malignant and 795 benign nodules from the LIDC/IDRI database.

Main Results:

  • The fusion algorithm consistently achieved superior performance across all tested CNN architectures.
  • Achieved the highest AUC, accuracy, sensitivity, and specificity compared to other classification models.
  • Demonstrated the complementary benefits of HF and CNN features, reducing reliance on large annotated datasets.

Conclusions:

  • The proposed fusion algorithm offers a robust and accurate method for predicting lung nodule malignancy in LDCT screening.
  • This approach enhances diagnostic capabilities, potentially improving lung cancer detection rates and patient outcomes.