Abstract
Today, the analysis of medical images has gained importance with the advancement of technology. The optical coherence tomography (OCT) imaging technique, one of the medical imaging methods, is prevalent in retinal imaging. With the analysis of retinal images, some age-related and diabetes-related diseases can be detected in the macular layer, which is essential for the human visual system. In the study, standard retinal images and retinal images of choroidal neovascularization (CMV), drusen, diabetic macular edema (DME) diseases from an open-access website (kaggle) were used as a dataset. The architecture that gives the highest accuracy in disease diagnosis is proposed by applying deep learning methods ResNet-152, HitNet, Efficient-B0 and Efficient-B7 on the data set. Among these three architectures, the ResNet-152 architecture gave the most successful result with an accuracy rate of 99.17%. In addition, an ensemble model was created under the name of RDV-Net in the study. RDV-Net; It showed a high classification success rate of 99.78% on the Mnist dataset and 99.12% on the Cifar-10 dataset. When RDV-Net was applied to the data set used in the study, 99.59% test success was achieved.
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Acknowledgements
We want to thank everyone who made the Retinal OCT Images (optical coherence tomography) open-source data used in the study available on the website (kaggle).
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Aksoy, B., Ateş, F., Salman, O.K.M., Armağan, H., Soyaltin, E., Özcan, E. (2023). An Example Application for Early Diagnosis of Retinal Diseases Using Deep Learning Methods. In: Smart Applications with Advanced Machine Learning and Human-Centred Problem Design. ICAIAME 2021. Engineering Cyber-Physical Systems and Critical Infrastructures, vol 1. Springer, Cham. https://doi.org/10.1007/978-3-031-09753-9_2
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DOI: https://doi.org/10.1007/978-3-031-09753-9_2
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