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Development of a retinal pathology classification model using transfer learning and open datasets

Year: 2025

Type: article

Abstract: The retina is a multilayer structure that perceives and transmits visual stimuli to the brain via the optic nerve; however, its complex structure makes it vulnerable to damage. Optical coherence tomography (OCT) is the gold standard for diagnosing retinal pathologies. This study aimed to develop a deep learning model for the classification of retinal pathologies based on OCT data. The study design included the use of transfer learning and training a model with a ResNet-50 architecture on three open datasets containing over 110.000 OCT images and 10 retinal condition classes, including normal and 10 pathological conditions. As a result, the model demonstrated a high classification accuracy of 95 %. Thus, transfer learning and significant dataset expansion provide high classification model accuracy. The study also highlights that open access to data significantly impacts the development of artificial intelligence technologies in healthcare.

Source: Cybernetics and Physics

DOI: 10.35470/2226-4116-2025-14-2-95-102

Authors: Lyubov E. Aksenova, Kirill D. Aksenov, Anton V. Prisyazhnyuk, Victoria V. Myasnikova, Andrey V. Krasov

Institutions: Kuban State Technological University, Maikop State Technological University, Saint-Petersburg State University of Telecommunications

Language: English

Topic: Retinal Imaging and Analysis

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: diamond

OpenAlex ID: W4414640156

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