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SAAM-VetNet: an attention-based multi-task framework for animal disease detection and severity grading
Ishana Attri1, Brij Vanita2, Rajesh Rajput2
1School of Computer Science and Engineering, Galgotias University, Uttar Pradesh, India.
Annals of Medicine and Surgery (2012)
|November 3, 2025
Summary
We developed SAAM-VetNet, a deep learning framework for detecting animal diseases and their severity from medical images. This novel approach improves diagnostic accuracy in veterinary medicine and preclinical research.
Area of Science:
- Veterinary Medicine
- Artificial Intelligence
- Medical Imaging
Background:
- Early detection of animal diseases is crucial for welfare and research.
- Current diagnostic methods can be time-consuming and require specialized expertise.
Purpose of the Study:
- To introduce SAAM-VetNet, a novel deep learning framework for simultaneous animal disease detection and severity grading.
- To enhance diagnostic accuracy and interpretability in veterinary and preclinical settings.
Main Methods:
- Developed a Severity-Aware Attention-Based Multi-Task (SAAM-VetNet) deep learning framework.
- Integrated a convolutional block attention module and a multi-branch learning strategy.
- Evaluated the model on two public datasets: Animal Disease Classification and Mastitis Disease Detection.
Main Results:
- SAAM-VetNet achieved 91.2% accuracy and an 89.8% F1 score.
- Outperformed established models like ResNet18, MobileNetV2, EfficientNet-B0, DenseNet121, and Vision Transformer (ViT).
- Demonstrated significant enhancement in model interpretability and diagnostic accuracy.
Conclusions:
- Attention mechanisms and severity-aware multi-task learning improve diagnostic performance.
- SAAM-VetNet offers a robust tool for automated veterinary diagnostics and preclinical model selection.
