CNN model-based image classification for canine brain MRI abnormalities
Abstract
Diagnostic imaging represents one of the most promising clinical applications of artificial intelligence (AI) algorithms. The purpose of this study was to evaluate the customised convolutional neural network (CNN) SepNetDense for distinguishing between normal and abnormal canine brain magnetic resonance images (MRIs), with the aim of enhancing diagnostic efficiency and assisting radiologists in identifying abnormal images. The dataset comprised T1-weighted (T1w) pre- and post-contrast sequences in transverse, sagittal, and dorsal planes from 550 dogs, collected from four universities. Dogs were included if they had a complete clinical diagnosis confirmed either through histopathology or in accordance with current clinical consensus. Patients were randomly divided into a training set (n = 444), a validation set (n = 53), and a test set (n = 53). Each MRI was labelled on a slice-by-slice basis as normal or abnormal. The model was trained on 205 normal imaging datasets (e.g., extracranial aetiologies, idiopathic epilepsy, paroxysmal dyskinesia) and 239 abnormal ones (e.g., neoplasms, inflammatory lesions, other pathologies). The model correctly predicted 74% of the true normal slices in the test set as normal and 73% of the true abnormal as abnormal. A ROC analysis of the model’s prediction at the patient level revealed that, at a threshold of 51% abnormal slices per patient, the model reached an optimal balance of 83% sensitivity (95% CI 67–91%) and 78% (95% CI 54–91%) specificity, with a balanced accuracy of 80% (95% CI 69–92%). ANCOVA revealed that the CNN’s classification performance was influenced by multiple biological, technical, and institutional factors. Lesion diagnosis, institutional setting, and breed size had significant large effects, whereas body weight showed a significant medium effect. Significant interactions with large effect sizes were also observed between diagnosis and institute, as well as between breed size and weight. Additionally, a distinct pattern was observed in the distribution of prediction categories along the anatomical axes, with a trend towards better CNN performance in the central quartiles across all MRI sequences. Overall, the results demonstrate the feasibility of CNN-assisted triage of canine brain MRI studies and highlight diagnosis, institutional setting, breed size, and body weight as key determinants of model performance, emphasising the importance of heterogeneous datasets for the development of robust veterinary AI applications.
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BibTeX
@article{abani2026modelbased,
title = {{CNN model-based image classification for canine brain MRI abnormalities}},
author = {Samira Abani and Merlin Laue and Peter J. Dickinson and Steven De Decker and Rodrigo Gutierrez-Quintana and Siavash Ghiasvand and Sahar Tahmasebi and Franziska Spohn and Alexander Sabbotin and Alexej Hänsch and Jörg Janisch and Reiner Ulrich and Ehren McLarty and Jasmin Nessler and Holger Volk},
journal = {Scientific Reports},
year = {2026},
month = oct,
doi = {10.1038/s41598-026-71470-w},
url = {https://www.nature.com/articles/s41598-026-71470-w},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - CNN model-based image classification for canine brain MRI abnormalities
AU - Samira Abani
AU - Merlin Laue
AU - Peter J. Dickinson
AU - Steven De Decker
AU - Rodrigo Gutierrez-Quintana
AU - Siavash Ghiasvand
AU - Sahar Tahmasebi
AU - Franziska Spohn
AU - Alexander Sabbotin
AU - Alexej Hänsch
AU - Jörg Janisch
AU - Reiner Ulrich
AU - Ehren McLarty
AU - Jasmin Nessler
AU - Holger Volk
PY - 2026
DA - 2026/10/23
JO - Scientific Reports
DO - 10.1038/s41598-026-71470-w
UR - https://www.nature.com/articles/s41598-026-71470-w
AB - Diagnostic imaging represents one of the most promising clinical applications of artificial intelligence (AI) algorithms. The purpose of this study was to evaluate the customised convolutional neural network (CNN) SepNetDense for distinguishing between normal and abnormal canine brain magnetic resonance images (MRIs), with the aim of enhancing diagnostic efficiency and assisting radiologists in identifying abnormal images. The dataset comprised T1-weighted (T1w) pre- and post-contrast sequences in transverse, sagittal, and dorsal planes from 550 dogs, collected from four universities. Dogs were included if they had a complete clinical diagnosis confirmed either through histopathology or in accordance with current clinical consensus. Patients were randomly divided into a training set (n = 444), a validation set (n = 53), and a test set (n = 53). Each MRI was labelled on a slice-by-slice basis as normal or abnormal. The model was trained on 205 normal imaging datasets (e.g., extracranial aetiologies, idiopathic epilepsy, paroxysmal dyskinesia) and 239 abnormal ones (e.g., neoplasms, inflammatory lesions, other pathologies). The model correctly predicted 74% of the true normal slices in the test set as normal and 73% of the true abnormal as abnormal. A ROC analysis of the model’s prediction at the patient level revealed that, at a threshold of 51% abnormal slices per patient, the model reached an optimal balance of 83% sensitivity (95% CI 67–91%) and 78% (95% CI 54–91%) specificity, with a balanced accuracy of 80% (95% CI 69–92%). ANCOVA revealed that the CNN’s classification performance was influenced by multiple biological, technical, and institutional factors. Lesion diagnosis, institutional setting, and breed size had significant large effects, whereas body weight showed a significant medium effect. Significant interactions with large effect sizes were also observed between diagnosis and institute, as well as between breed size and weight. Additionally, a distinct pattern was observed in the distribution of prediction categories along the anatomical axes, with a trend towards better CNN performance in the central quartiles across all MRI sequences. Overall, the results demonstrate the feasibility of CNN-assisted triage of canine brain MRI studies and highlight diagnosis, institutional setting, breed size, and body weight as key determinants of model performance, emphasising the importance of heterogeneous datasets for the development of robust veterinary AI applications.
SN - 2045-2322
ER -