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Pneumonia Detection in Veterinary X-Rays Utilizing PaddleSeg-Based Semantic Segmentation and Mamba-Powered

Xiaobao Li1, Binbin Li1

  • 1Vetidia Intelligent Technology Co., Ltd, Beijing, China.

Veterinary Radiology & Ultrasound : the Official Journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association
|July 14, 2026
PubMed
Summary

This study introduces an AI diagnostic pipeline for pet pneumonia detection using X-rays. The AI system achieves high accuracy, improving early diagnosis and treatment outcomes for companion animals.

Keywords:
AI Risk Index (ARI)AI‐drivenGradient‐weighted Class Activation Mapping (GradCAM)classificationpneumonia

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Area of Science:

  • Veterinary Radiology
  • Artificial Intelligence in Medicine
  • Companion Animal Health

Background:

  • Pneumonia is a major cause of illness and death in pets, with diagnosis often delayed.
  • Manual interpretation of veterinary X-rays has high variability and low sensitivity for early pneumonia.
  • AI offers a potential solution to improve diagnostic accuracy and efficiency in veterinary medicine.

Purpose of the Study:

  • To develop and evaluate an AI-driven diagnostic pipeline for detecting pneumonia in companion animals using radiographic images.
  • To improve the accuracy and consistency of pneumonia diagnosis in pets, leading to better treatment outcomes.
  • To create an interpretable and safe AI system for veterinary applications.

Main Methods:

  • A six-part AI pipeline was developed: data acquisition, preprocessing, semantic segmentation (PaddleSeg), classification (Mamba framework), risk estimation, and explainability (GradCAM).
  • Semantic segmentation achieved a mean intersection over union (mIoU) of 0.8999 for chest area extraction.
  • The Mamba framework was used for classification, achieving high accuracy and F1-scores on both frontal and lateral X-ray projections.

Main Results:

  • The AI model demonstrated strong performance in pneumonia classification for both VD/DV and lateral projections.
  • For VD/DV projections, accuracy was 90.35%, macro-F1 score 0.9034, and AUC 0.9438.
  • For lateral projections, accuracy was 89.8%, macro-F1 score 0.8965, and AUC 0.9361.
  • A novel AI Risk Index (ARI) was proposed for integrated uncertainty and interpretability.

Conclusions:

  • The developed AI diagnostic pipeline significantly enhances pneumonia detection in companion animals.
  • The AI system offers high accuracy and robustness, comparable to or exceeding human interpretation.
  • This work represents a significant advancement in deploying interpretable and safe AI in veterinary medicine.