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June 6, 2026Open Veterinary JournalOpen Access

Differentiation of common histological lesions in bovine liver using convolutional neural network-based deep learning

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Authors

SISusumu IwaideKKKumiko KIMURARSRyo Sugiura

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Overview

Randomized trial demonstrates effective classification of liver lesions in bovine, suggesting improved diagnostic capabilities.

Key Points

  • This study aims to classify common histological lesions in bovine livers using a convolutional neural network for improved pathological diagnosis.
  • Prepared 10 bovine cases across 4 groups: lymphoma, necrosis, fibrosis, and normal liver.
  • Scanned liver slides into whole slide images and collected patches, with 80% for training and 20% for testing.
  • Trained a DenseNet-based convolutional neural network and evaluated its performance on a test set.
  • Achieved 84.5% classification accuracy on the test data and an average F1-score of 83.5%.
  • Precision and recall: lymphoma (92.6%, 50%), necrosis (95.8%, 92.0%), fibrosis (89.8%, 97.0%), normal liver (69.7%, 99.0%).
  • Highlighted strong performance in classifying necrosis and fibrosis with room for improvement in lymphoma detection.

Cite This Study

Iwaide et al. (2026) studied this question.

synapsesocial.com/papers/6a23b83e71a5da9775e74774https://doi.org/10.5455/ovj.2026.v16.i5.65
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