Formation of a dataset for a neural network model for recognizing ophthalmological pathology in fundus images

Introduction. In ophthalmological practice, the use of digital diagnostic devices has allowed us to accumulate a large database of medical images, and machine learning algorithms make it possible to use this data to create automated solutions that increase the speed, efficiency and quality of screening studies.

Materials and methods. In this study, data collection and the formation of a dataset of digital images of the fundus were carried out from open cloud databases for storing and processing data Kaggle, Mendeley Data, Figshare. All images were depersonalized and pseudonymized in accordance with the Federal Law of July 27, 2006 N 152-FZ "On Personal Data" and GOST R 55036-2012 / ISO / TS 25237: 2008.

Results. The collected dataset included 1765 digital fundus images measuring 640 × 480 and 2048 × 2048 pixels and was renamed the final dataset "OcuDate". The images were annotated by quality classes and a data preprocessing tool was developed, which will subsequently allow training the neural network model.

Conclusions. The obtained data can be integrated into the fundus image analysis and processing system when creating an expert system for supporting clinical decision-making by ophthalmologists.