What question did this study set out to answer?

To evaluate the effectiveness of ResNet50V2 in diagnosing retinal detachment in dogs using fundus photographs.

April 10, 2026Open Access

A Comparative Analysis of Deep Convolutional Networks for Automated Diagnosis of Retinal Detachment in Dogs

Key Points

To evaluate the effectiveness of ResNet50V2 in diagnosing retinal detachment in dogs using fundus photographs.
Used ResNet50V2 for analysis of canine fundus images.
Conducted a comparative analysis against other deep learning models.
Evaluated performance for automated diagnosis of retinal detachment.
ResNet50V2 showed strong potential in identifying retinal detachment.
Suggests scalability for use in veterinary practices for screening.
Further validation needed across different devices and settings.

Abstract

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.

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Authors

Sıtkıcan Okur

Atatürk University

Büşra Baykal

Atatürk University

Yasemin Akçora

Atatürk University

Journals

Veterinary Ophthalmology

Actions

Institutions

Atatürk University

Adnan Menderes University

Burdur Mehmet Akif Ersoy Üniversitesi

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A Comparative Analysis of Deep Convolutional Networks for Automated Diagnosis of Retinal Detachment in Dogs

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Abstract

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