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Scoring pleurisy in slaughtered pigs using convolutional neural networks.

Abigail R Trachtman1, Luca Bergamini2, Andrea Palazzi2

  • 1Faculty of Veterinary Medicine, University of Teramo, Loc. Piano d'Accio, 64100, Teramo, Italy.

Veterinary Research
|April 12, 2020
PubMed
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Automated scoring of pleurisy in slaughtered pigs using convolutional neural networks achieved 85.5% accuracy. This technology offers a fast, cost-effective method for systematic lesion recording in the swine industry.

Area of Science:

  • Veterinary Medicine
  • Animal Science
  • Artificial Intelligence

Background:

  • Respiratory diseases significantly impact the global swine industry's profitability.
  • Slaughterhouses are critical checkpoints for assessing pig health and collecting epidemiological data.
  • Manual scoring of lesions in slaughtered pigs is time-consuming and costly, hindering systematic recording.

Purpose of the Study:

  • To develop and train a convolutional neural network (CNN)-based system for automated scoring of pleurisy in slaughtered pigs.
  • To enable systematic examination of all slaughtered livestock for respiratory lesions.
  • To provide a valuable feedback mechanism for swine farms and epidemiological studies.

Main Methods:

  • Training a convolutional neural network (CNN) model.

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  • Utilizing images of pig half carcasses at slaughter.
  • Automating the scoring of pleurisy lesions.

    • Main Results:

    • The CNN-based system achieved an overall accuracy of 85.5% in differentiating between healthy and pleurisy-affected half carcasses.
    • The system demonstrated higher accuracy in identifying severely affected carcasses compared to those with milder lesions.
    • The automated system shows potential for systematic lesion recording.

    Conclusions:

    • CNN-based technology can automate the scoring of pleurisy in slaughtered pigs.
    • This automation offers a fast and affordable tool for systematic lesion recording.
    • Further development includes training CNNs for pneumonia scoring and conducting trials in large-scale slaughterhouses.