Abstract
Nowadays the rapid development of the Internet of Medical Things using machine learning and image processing techniques, computer-aided diagnosis systems have become crucial in the medical field, particularly in ophthalmology. Optical coherence tomography (OCT) is a powerful image technology used to diagnose retinal eye diseases. The macular layer in the human visual system analyzes retinal images to detect diabetes-related and age-related diseases. This paper proposes a hybrid ensemble deep network (HEDN) model for predicting retinal eye diseases. We utilized the retinal OCT dataset acquired from the “Shiley Eye Institute, University of California’s San Diego” to integrate three classification designs, namely the MobileNet V2 model, ResNet50 model, and VGG16 model, to enhance the classification of retinal pathologies in various stages. The experimental results show that the proposed HEDN model detected retinal pathologies in all stages compared to other existing methods. The experimentation results revealed that the proposed HEDN method attained a higher accuracy, precision, sensitivity, specificity, and F1-measure as 97.3%, 97%, 96.6%, 96.2%, and 96.5%. Also the training loss of 0.05, testing loss of 0.07, training accuracy of 0.973, testing accuracy of 0.935, AUC/ROC of 0.962, and kappa coefficient of 0.85 enhanced the proposed method with better effectiveness.
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Priya, J.S., Angayarkanni, S.A., Balakiruthiga, B. et al. Predicting retinal pathologies with IoMT-enabled hybrid ensemble deep network model. SIViP 17, 4255–4264 (2023). https://doi.org/10.1007/s11760-023-02658-0
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DOI: https://doi.org/10.1007/s11760-023-02658-0