Comparative image-based evaluation of deep learning models for the diagnosis of different ocular diseases in cats

Ocular pathologies are a leading cause of visual impairment in cats and require accurate, timely diagnosis for effective treatment. This study aimed to comparatively evaluate the performance of different deep learning (DL) architectures for the automated identification and classification of feline ophthalmic disease. A dataset of 456 clinically validated ocular images, spanning 13 classes (12 ocular diseases and a healthy group), was used to train nine convolutional neural network (CNN) architectures, including ResNet, EfficientNet, DenseNet, MobileNet, and VGG models, through transfer learning with pre-trained ImageNet weights. Data augmentation and five-fold cross-validation were applied during training. Statistical significance of performance differences across architectures was assessed using Cohen's Kappa and McNemar's test. On the held-out test set, EfficientNet-B0 reached the highest classification accuracy (78%), while DenseNet-121 obtained the highest macro-averaged F1-score (0.76) and Area Under the Curve (AUC) (0.9790), followed by EfficientNet-B0 (0.9750) and ResNet-34 (0.9724). Confusion matrix analysis showed the strongest classification consistency for cherry eye, healthy, and corneal sequestration (precision and recall reaching 1.00 in several cases), and the weakest for glaucoma (recall 0.25) and corneal ulcer (F-1 score 0.29). McNemar's test indicated no statistically significant difference among EfficientNet-B0, DenseNet-121, and ResNet-34 (p > 0.05), and per-image inference times across all nine architectures ranged from 2.67 to 5.73 ms. Gradient-weighted Class Activation Mapping (Grad-CAM) visualizations applied to the EfficientNet-B0 model confirmed that the model focused on clinically relevant ocular regions during classification. Overall, the results obtained with EfficientNet-B0, DenseNet-121, and ResNet-34 suggest that DL-based multi-class classification could support clinical decision-making in the diagnosis of feline ophthalmic diseases.