Hybrid adaptive deep learning classifier for early detection of diabetic retinopathy using optimal feature extraction and classification

Objectives Diabetic retinopathy (DR) is one of the leading causes of blindness. It is important to use a comprehensive learning method to identify the DR. However, comprehensive learning methods often rely heavily on encrypted data, which can be costly and time consuming. Also, the DR function is no...

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Bibliographic Details
Published in:Journal of diabetes and metabolic disorders 2023-04, Vol.22 (1), p.881-895
Main Authors: Hemanth, S. V., Alagarsamy, Saravanan
Format: Article
Language:English
Subjects:
Blindness
Diabetes
Endocrinology
Medicine
Medicine & Public Health
Metabolic Diseases
Neural networks
Research Article
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Summary:Objectives Diabetic retinopathy (DR) is one of the leading causes of blindness. It is important to use a comprehensive learning method to identify the DR. However, comprehensive learning methods often rely heavily on encrypted data, which can be costly and time consuming. Also, the DR function is not displayed and is scattered in the high-definition image below. Methods Therefore, learning how to distribute such DR functions is a big challenge. In this work, we proposed a hybrid adaptive deep learning classifier for early detection of diabetic retinopathy (HADL-DR). First, we provide an improved multichannel-based generative adversarial network (MGAN) with semi-maintenance to detect blood vessels segmentation. Results By reducing the reliance on the encoded data, the following high-resolution images can be used to detect the indivisible features of some semi-observed MGAN references. Scale invariant feature transform (SIFT) function is then extracted and the best function is selected using the improved sequential approximation optimization (SAO) algorithm. After that, a hybrid recurrent neural network with long short-term memory (RNN-LSTM) is utilized for DR classification. The proposed RNN-LSTM classifier evaluated through standard benchmark Kaggle and Messidor datasets. Conclusion Finally, the simulation results are compared with the existing state-of-art classifiers in terms of accuracy, precision, recall, f-measure and area under cover (AUC), it is seen that more successful results are obtained.
ISSN:2251-6581
2251-6581
DOI:10.1007/s40200-023-01220-6