Open veterinary journal · 2026
Differentiation of common histological lesions in bovine liver using convolutional neural network-based deep learning.
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Iwaide Susumu, Kimura Kumiko, Sugiura Ryo, Oishi Yu, Shibahara Tomoyuki
Аннотация
BACKGROUND: Liver lesions occur in livestock due to various causes, including infection and poisoning. Liver diseases can affect multiple organs, making accurate and efficient pathological diagnosis essential. Therefore, a model capable of automatically classifying various lesions could assist in diagnosis.
AIM: This study aimed to classify common lesions in bovine livers-lymphoma, necrosis, and fibrosis-using deep learning based on a convolutional neural network to develop a potential pathological diagnosis support model.
METHODS: We prepared 10 bovine cases for four groups: lymphoma, necrosis, fibrosis, and normal liver. After scanning the slides as whole slide images, we divided the images into patches and collected 50 patches per slide containing the target tissue. The patches were split into 2 groups: training cross-validation (80%) and test (20%) groups. A DenseNet-based convolutional neural network was trained via cross-validation, and its performance was evaluated with the test set.
RESULTS: The model achieved a classification accuracy of 84.5% on the test data with an average F1-score of 83.5% across the four labels. The precision and recall for each label were 92.6% and 50% (lymphoma), 95.8% and 92.0% (necrosis), 89.8% and 97.0% (fibrosis), and 69.7% and 99.0% (normal liver), respectively.
CONCLUSION: Image classification using a deep learning model based on a convolutional neural network showed promising performance for classifying common lesions in bovine livers, particularly necrosis and fibrosis. However, further improvement is required for reliable lymphoma detection.
AIM: This study aimed to classify common lesions in bovine livers-lymphoma, necrosis, and fibrosis-using deep learning based on a convolutional neural network to develop a potential pathological diagnosis support model.
METHODS: We prepared 10 bovine cases for four groups: lymphoma, necrosis, fibrosis, and normal liver. After scanning the slides as whole slide images, we divided the images into patches and collected 50 patches per slide containing the target tissue. The patches were split into 2 groups: training cross-validation (80%) and test (20%) groups. A DenseNet-based convolutional neural network was trained via cross-validation, and its performance was evaluated with the test set.
RESULTS: The model achieved a classification accuracy of 84.5% on the test data with an average F1-score of 83.5% across the four labels. The precision and recall for each label were 92.6% and 50% (lymphoma), 95.8% and 92.0% (necrosis), 89.8% and 97.0% (fibrosis), and 69.7% and 99.0% (normal liver), respectively.
CONCLUSION: Image classification using a deep learning model based on a convolutional neural network showed promising performance for classifying common lesions in bovine livers, particularly necrosis and fibrosis. However, further improvement is required for reliable lymphoma detection.