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

You might also read

Related Articles

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

Sort by
Same author

Transnasal balloon-assisted posterior ridge restoration for inferior orbital wall fractures.

Archives of craniofacial surgery·2026
Same author

Non-ablative transurethral laser treatment for collagen remodeling with functional recovery in an in vivo model of stress urinary incontinence.

Scientific reports·2026
Same author

Hierarchical silver-tannic acid hydrophobic coating via layer-by-layer assembly for antibiofilm applications on urinary catheters.

Materials today. Bio·2026
Same author

Investigation of shockwave treatment for disruption of bacterial biofilm on tubular structure.

Scientific reports·2025
Same author

Theranostic Polyaniline-Integrated <i>N</i>-Acetyl-l-Cysteine Hydrogel for Synergistic Photothermal Antibacterial Therapy and Enhanced Cell Migration.

ACS applied bio materials·2025
Same author

Investigation of therapeutic potential of simultaneous triple-wavelength laser technology for skin rejuvenation.

Lasers in medical science·2025

Related Experiment Video

Updated: Jun 15, 2025

Electrospun Nanofiber Scaffolds with Gradations in Fiber Organization
09:32

Electrospun Nanofiber Scaffolds with Gradations in Fiber Organization

Published on: April 19, 2015

9.8K

Geometric feature extraction in nanofiber membrane image based on convolution neural network for surface roughness

Dong Hee Kang1,2, Na Kyong Kim3, Wonoh Lee1

  • 1Department of Mechanical Engineering, Chonnam National University, 77 Youngbong-ro, Buk-Gu, Gwangju, 61186, Republic of Korea.

Heliyon
|August 22, 2024
PubMed
Summary

This study introduces a novel artificial intelligence method using convolution neural networks (CNNs) to analyze membrane surface roughness from images. The approach accurately extracts geometric features, offering a more comprehensive surface characterization than traditional techniques.

Keywords:
Average surface roughnessConvolution neural networkFeature extractionImage preprocessing

More Related Videos

Iron Nanowire Fabrication by Nano-Porous Anodized Aluminum and its Characterization
07:14

Iron Nanowire Fabrication by Nano-Porous Anodized Aluminum and its Characterization

Published on: October 6, 2019

8.3K
Multiscale Structures Aggregated by Imprinted Nanofibers for Functional Surfaces
06:14

Multiscale Structures Aggregated by Imprinted Nanofibers for Functional Surfaces

Published on: September 11, 2018

6.5K

Related Experiment Videos

Last Updated: Jun 15, 2025

Electrospun Nanofiber Scaffolds with Gradations in Fiber Organization
09:32

Electrospun Nanofiber Scaffolds with Gradations in Fiber Organization

Published on: April 19, 2015

9.8K
Iron Nanowire Fabrication by Nano-Porous Anodized Aluminum and its Characterization
07:14

Iron Nanowire Fabrication by Nano-Porous Anodized Aluminum and its Characterization

Published on: October 6, 2019

8.3K
Multiscale Structures Aggregated by Imprinted Nanofibers for Functional Surfaces
06:14

Multiscale Structures Aggregated by Imprinted Nanofibers for Functional Surfaces

Published on: September 11, 2018

6.5K

Area of Science:

  • Materials Science
  • Artificial Intelligence
  • Image Analysis

Background:

  • Traditional surface roughness measurement methods (e.g., atomic force microscopy, optical profiler) have limitations in analyzing porous membranes due to restricted analysis areas and depth resolution.
  • Scanning electron microscopy (SEM) images, when combined with advanced feature extraction, offer potential for broader surface roughness analysis across various resolutions.

Purpose of the Study:

  • To develop and validate an artificial intelligence-based method for extracting average surface roughness from membrane images.
  • To leverage convolution neural network (CNN) models for analyzing geometric characteristics and improving surface roughness measurement accuracy.

Main Methods:

  • Image preprocessing techniques were applied to enhance geometric patterns by amplifying pixel intensity disparities.
  • A feature map-based CNN model was employed for feature extraction from membrane images.
  • Statistical analysis and magnitude spectrum were used to classify surface roughness and predict logarithmic average surface roughness values.

Main Results:

  • The CNN model achieved a Mean Absolute Percentage Error (MAPE) of 4.80% on the test dataset for predicting logarithmic average surface roughness.
  • Geometric patterns and magnitude spectrum analysis confirmed the classification of surface roughness.
  • The method demonstrated effective indirect surface measurement by analyzing geometric patterns.

Conclusions:

  • The proposed CNN-based feature extraction combined with statistical analysis provides a valuable indirect method for measuring surface roughness.
  • This approach effectively reveals hidden physical characteristics in surface geometries from irregular pixel patterns.
  • The technique reduces randomness error in structural characteristic analysis and offers a more comprehensive understanding of surface topography.