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Calcium-Scoring CT ScanA calcium-scoring CT scan, also known as coronary artery calcium (CAC) scan, detects calcium deposits in the coronary arteries. This test assesses the risk of coronary artery disease (CAD), which can lead to cardiovascular events such as angina, heart failure, and sudden cardiac arrest.A calcium-scoring CT scan is generally recommended for individuals at intermediate risk of CAD without symptoms. It includes:Men aged 40-75 and women aged 50-75: Especially those with a...
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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
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CAT-CBAM-Net: An Automatic Scoring Method for Sow Body Condition Based on CNN and Transformer.

Hongxiang Xue1,2, Yuwen Sun1,2, Jinxin Chen1,2

  • 1College of Engineering, Nanjing Agricultural University, Nanjing 210031, China.

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This study introduces an automatic sow body condition scoring system using a dual neural network. The AI model accurately assesses sow health, improving reproductive performance and farm management efficiency.

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Area of Science:

  • Animal Science
  • Computer Science
  • Artificial Intelligence

Background:

  • Sow body condition scoring is crucial for optimizing nutrition and reproductive performance in swine farming.
  • Manual assessment methods are labor-intensive and time-consuming, particularly in large-scale operations.
  • Accurate body condition scoring directly impacts sow health and productivity.

Purpose of the Study:

  • To develop an automated system for sow body condition scoring using artificial intelligence.
  • To improve the efficiency and accuracy of sow body condition assessment in commercial swine farms.
  • To enhance the capture of both local and global features in sow images for better analysis.

Main Methods:

  • A dual neural network architecture combining Convolutional Neural Networks (CNNs) and transformer networks was employed.
  • A Channel-Attention module (CBAM) was integrated to focus on relevant image features.
  • An optimized focal loss function was utilized to address data imbalance and mislabeling issues.

Main Results:

  • The developed method achieved high performance metrics: 91.06% average precision, 91.58% average recall, and 91.31% average F1 score.
  • Comparative experiments confirmed the proposed method's superior performance on the dataset.
  • The system demonstrated effective automatic scoring of sow body condition.

Conclusions:

  • The AI-powered system offers a significant advancement over manual sow body condition scoring.
  • This technology has promising applications for improving efficiency and accuracy in swine management.
  • Automated scoring can lead to better-informed nutritional decisions and enhanced sow reproductive outcomes.