A comparative study of deep convolutional neural networks for the analysis of retinal damage in optical coherence tomography

Anastasiia Rozhyna; Manfredo Atzori; Henning Müller

doi:10.1117/12.3006872

2 April 2024 A comparative study of deep convolutional neural networks for the analysis of retinal damage in optical coherence tomography

Anastasiia Rozhyna, Manfredo Atzori, Henning Müller

Author Affiliations +

Proceedings Volume 12931, Medical Imaging 2024: Imaging Informatics for Healthcare, Research, and Applications; 129310R (2024) https://doi.org/10.1117/12.3006872
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

Diet, lifestyle and an aging population have led to many diseases, some of which can be seen well in the eyes and analyzed by simple means, such as OCT (Optical Coherence Tomography) scans. This article presents a comparative study examining transfer learning methods for classifying retinal OCT scans. The study focuses on the classification of several retina alterations such as Age-related Macular Degeneration (AMD), Choroidal Neovascularization (CNV), Diabetic Macular Edema (DME) and normal cases. The approach was evaluated on a large dataset of labeled OCT scans. In this work we use CNN architectures such as VGG16, VGG19, ResNet50, MobileNet, InceptionV3 and Xception with the weights pre-trained on ImageNet and then fine-tuned on the domain-specific data. The results indicate that the proposed transfer learning is a powerful tool for classifying multi-class retinal OCT scans.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Anastasiia Rozhyna, Manfredo Atzori, and Henning Müller "A comparative study of deep convolutional neural networks for the analysis of retinal damage in optical coherence tomography", Proc. SPIE 12931, Medical Imaging 2024: Imaging Informatics for Healthcare, Research, and Applications, 129310R (2 April 2024); https://doi.org/10.1117/12.3006872

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