Dr. Dražen Draškovic Profile

Dr. Dražen Draškovic

at Univ of Belgrade

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SPIE Journal Paper | 30 November 2022 Open Access

Retinal disease classification based on optical coherence tomography images using convolutional neural networks

Maša Stanojevic, Dražen Draškovic, Boško Nikolic

JEI, Vol. 32, Issue 03, 032004, (November 2022) https://doi.org/10.1117/12.10.1117/1.JEI.32.3.032004

KEYWORDS: Retinal diseases, Optical coherence tomography, Education and training, Network architectures, Convolution, Retina, Analytical research, Convolutional neural networks, Data modeling, Image classification

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The challenges in today’s medicine are progressively more related to the application of artificial intelligence and supervised learning techniques. Optical coherence tomography (OCT) is a noninvasive imaging technology used to obtain high-resolution cross-sectional images of the retina. The layers within the retina can be differentiated and retinal thickness can be measured to facilitate early detection and diagnosis of retinal diseases and conditions. This research paper is aimed at exploring different possibilities of applying deep learning, specifically convolutional neural networks in retinal diseases. During the research, several different architectures—AlexNet, VGG, Inception, and residual network, were evaluated, and the convolutional neural network that proved to be the most successful in the classification was based on the Inception architecture. Hyperparameter tuning was applied as the main method to find the most optimal solution. The key contributions of this research refer to the analysis of different architectures that can be applied in the classification of retinal diseases based on OCT images, as well as the evaluation of the test set obtained by comparing different models with different hyperparameters. This research yielded the best results obtained with Inception1, when training by means of the root mean square propagation optimizer with a batch size of 32, learning rate of 1e − 6, momentum of 0.99, and L2 regularization rate of 0.001. This model achieved an accuracy of 0.95528. In the conclusion of the paper, the advantages of the proposed and implemented solutions were discussed, and a proposal for further improvements was proposed.

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