A hybrid multilayered classification model with VGG-19 net for retinal diseases using optical coherence tomography images

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 ear...

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Bibliographic Details
Published in:Soft computing (Berlin, Germany) Germany), 2023-09, Vol.27 (17), p.12559-12570
Main Authors: Udayaraju, Pamula, Jeyanthi, P., Sekhar, B. V. D. S.
Format: Article
Language:English
Subjects:
Abnormalities
Accuracy
Age related diseases
Application of Soft Computing
Artificial Intelligence
Classification
Computational Intelligence
Control
Datasets
Disorders
Engineering
Eye diseases
Eye movements
Machine learning
Mathematical Logic and Foundations
Mechatronics
Medical imaging
Neural networks
Retinal images
Robotics
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Summary: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, F 1 score, and accuracy.
ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-023-08928-w