Authors : Jeewon Kim

Volume/Issue : Volume 10 - 2025, Issue 9 - September

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Google Scholar : https://tinyurl.com/jzayw2nr

Scribd : https://tinyurl.com/yshf59dw

DOI : https://doi.org/10.38124/ijisrt/25sep693

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Abstract : Veterinary radiology faces persistent hurdles for deep learning: limited labeled data within each species and substantial domain shift driven by anatomical, acquisition, and contrast differences. We investigate a domain adaptation framework that transfers a pneumonia detector trained on canine chest radiographs to feline radiographs, enabling accurate, dataefficient cross-species diagnosis without requiring large labeled target datasets. The approach integrates adversarial distribution alignment with optional semi-supervised fine-tuning, and supports deployment practices such as probability calibration and visual explanations.

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