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

Multi-robot coordination for underwater Mothership-Passenger systems.

Frontiers in robotics and AI·2026
Same author

Impact of Cross-Validation on Machine Learning Models for Early Detection of Intrauterine Fetal Demise.

Diagnostics (Basel, Switzerland)·2023
See all related articles

Related Experiment Video

Updated: Jul 26, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.5K

Healthy-unhealthy animal detection using semi-supervised generative adversarial network.

Shubh Almal1, Apoorva Reddy Bagepalli1, Prajjwal Dutta2

  • 1School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India.

Peerj. Computer Science
|June 22, 2023
PubMed
Summary

This study introduces a novel deep learning system for detecting unhealthy animals, achieving 91.4% accuracy. The method enhances image datasets and uses a semi-supervised generative adversarial network (SGAN) for accurate health detection.

Keywords:
Deep learningFuzzy inference systemHealthy animalSemi-supervised Generative adversarial networkUnhealthy animal

More Related Videos

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction
06:19

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

Published on: August 16, 2024

456
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

585

Related Experiment Videos

Last Updated: Jul 26, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.5K
Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction
06:19

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

Published on: August 16, 2024

456
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

585

Area of Science:

  • Veterinary Medicine
  • Computer Science
  • Machine Learning

Background:

  • Animal illness poses risks to human health through disease transmission.
  • Early human history shows recognition of animals as disease carriers.
  • Detecting unhealthy animals is crucial for public health and animal welfare.

Purpose of the Study:

  • To develop an automated system for detecting healthy versus unhealthy animals.
  • To improve upon existing methods for animal health assessment using computer vision.
  • To address the challenge of limited datasets in animal health image analysis.

Main Methods:

  • Utilized a deep learning approach for animal health detection.
  • Employed image augmentation techniques (flipping, scaling, orientation) to expand the dataset.
  • Implemented a fuzzy-based brightness correction method.
  • Applied a semi-supervised generative adversarial network (SGAN) for classification.

Main Results:

  • Achieved 91.4% accuracy in detecting healthy and unhealthy animals.
  • The method demonstrated efficacy on an augmented COCO dataset.
  • The system accurately identifies animal health status from images.

Conclusions:

  • A novel two-fold system for animal health detection was successfully developed.
  • The proposed system significantly advances the field of automated animal health assessment.
  • The approach is adaptable for various computer vision applications requiring animal health analysis.