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A Comparative Analysis of Deep Convolutional Networks for Automated Diagnosis of Retinal Detachment in Dogs
Sıtkıcan Okur1, Büşra Baykal1, Yasemin Akçora1
1Faculty of Veterinary Medicine, Department of Surgery, Atatürk University, Erzurum, Türkiye.
Objective:
To compare ImageNet-pretrained deep convolutional neural networks for automated detection of retinal detachment (RD) in canine fundus photographs.
Animals Studied:
Archived fundus images from 275 dogs.
Procedures:
In this multicenter retrospective study, 2000 color fundus photographs (793 RD; 1207 normal) acquired between 2020 and 2025 were included after quality filtering. Data were split at the patient level into training (80%) and an independent validation set (20%). Transfer learning was applied to three pretrained architectures (ResNet50V2, VGG16, EfficientNetB0) using standardized preprocessing and real-time augmentation. Performance on the validation set was assessed using accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). Ninety-five percent confidence intervals were estimated by bootstrapping.
Results:
ResNet50V2 achieved the best overall discrimination (accuracy 0.8909; AUC 0.9194), followed by EfficientNetB0 (accuracy 0.8182; AUC 0.8831). VGG16 showed limited reliability (accuracy 0.6182; AUC 0.6868) due to a high false-positive rate. Gradient-weighted class activation mapping indicated that the best-performing model consistently attended to regions consistent with retinal detachment.
Conclusions:
ResNet50V2-based analysis of canine fundus photographs shows strong potential as a scalable screening support tool for RD. Prospective external validation across additional devices and practice settings is warranted before routine clinical implementation.
