Hybrid CNN-Vision Transformer for Rabbit Gastric Dilation: Projection-Dependent Architectural Requirements in Veterinary Radiology

Markus Clauss, Kerstin Müller, Marcus Clauss, Henning Richter

Keywords: hybrid CNN-vision transformer, veterinary radiology, medical image classification, deep learning, transfer learning, explainable AI, temporal external validation, architectural comparison, Grad-CAM visualization, annotation quality assessment

TL;DR: First AI system for rabbit gastric imaging with hybrid CNN-Vision Transformer architectures, revealing projection-dependent performance and clinical-grade sensitivity (87-92%) validated on temporal external cohorts.

Abstract:

Despite rabbits being the third most popular companion animal, AI for rabbit diagnostics is entirely absent (0/422 veterinary AI publications, 2013-2024). We present the first systematic comparison of hybrid CNN-Vision Transformer architectures for gastric dilation classification on 679 multi-institutional rabbit radiographs (371 laterolateral, 308 ventrodorsal). Rigorous 5-fold cross-validation with external validation (60 images, 11-month separation) reveals projection-dependent architectural requirements: laterolateral projections show architectural equivalence (88.94-89.38% F1, 0.44% range), while ventrodorsal benefit from hybrid fusion (87.03% vs 84.27% pure CNN, +2.76%, Cohen’s d=0.78, exceptional 1.77% generalization gap). Expert validation of 213 misclassifications revealed 42% systematic annotation errors, suggesting true performance 3-5% higher. External validation confirms clinical-grade sensitivity (87-92%), suitable for emergency
triage.

Track: Track 2: ML by Muslim Authors

Submission Number: 12