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Distinguishing lumpy skin disease from coat patterns using morphological priors in deep learning.

Lili Bai1, Chaopeng Guo1, Zhe Zhang1

  • 1Software College, Northeastern University, Shenyang 110819, China.

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A new image analysis model aids in early lumpy skin disease (LSD) detection in cattle. This tool helps identify potential lesions on farms, improving surveillance and control measures for this costly disease.

Keywords:
Automated image analysisCattle health monitoringClinical decision supportFarm‑level disease surveillanceLumpy skin disease

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Area of Science:

  • Veterinary Medicine
  • Computer Vision
  • Animal Health

Background:

  • Lumpy skin disease (LSD) is a significant transboundary cattle disease causing economic losses.
  • Early detection of LSD on farms is difficult due to subtle or obscured lesions.
  • Mobile technology offers potential for image-based disease screening.

Purpose of the Study:

  • To develop a morphology-driven image analysis model for lumpy skin disease detection in cattle (LSDD).
  • To create a tool that assists in early, on-farm screening of cattle for LSD-compatible lesions.

Main Methods:

  • Developed a morphology-driven image analysis model (LSDD) incorporating texture refinement and morphology consistency modules.
  • Trained and tested the model using datasets reflecting diverse farm conditions, including varying illumination and coat patterns.
  • The system provides an image-level classification of 'healthy' or 'lesion'.

Main Results:

  • The LSDD model effectively highlights subtle nodules and suppresses background noise.
  • It distinguishes between circular/clustered lesions and elongated coat markings.
  • Extensive experiments demonstrated the model's practicality under varied farm conditions.

Conclusions:

  • The LSDD model serves as a practical, morphology-driven screening aid for herd-level monitoring.
  • It supports earlier identification of animals with LSD-compatible lesions.
  • This tool enhances on-farm surveillance and facilitates timely control measure implementation.