Abstract
Retinal diseases are most widely affected by various types of people without any proper reason. Nowadays, many retinal diseases are identified by experts. Detecting retinal diseases in the early stages is very important with better accuracy. Deep learning (DL) techniques are commonly used in the early prediction of retinal disorders. In DL, multiple layers accurately detect abnormalities in the retinal images. Various datasets are also present for this research work. This paper uses a hybrid multilayered classification (HMLC/CNN-VGG19). This system is developed to categorize four kinds of retinal disorders (age-related macular degeneration, choroidal neovascularization, Drusen, diabetic retinopathy, as well as typical cases). The proposed H.M.L.C. is applied to OCT to gather pictures from different data sources, like the U.C.I. repository, Kaggle, etc. The CNN and VGG-19 models used in the H.M.L.C. are implemented in Python over the datasets. The experimental results in terms of classification accuracy are verified. The classification accuracy is high since the H.M.L.C. used the advanced features from CNN and VGG-19 models. The performance is calculated using sensitivity, specificity, F1 score, and accuracy.
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Pamula Udayaraju declares that he/she has no conflict of interest, P Jeyanthi declares that he/she has no conflict of interest, and B V D S Sekhar declares that he/she has no conflict of interest.
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Udayaraju, P., Jeyanthi, P. & Sekhar, B.V.D.S. A hybrid multilayered classification model with VGG-19 net for retinal diseases using optical coherence tomography images. Soft Comput 27, 12559–12570 (2023). https://doi.org/10.1007/s00500-023-08928-w
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DOI: https://doi.org/10.1007/s00500-023-08928-w