{"title":"Hybrid adaptive deep learning classifier for early detection of diabetic retinopathy using optimal feature extraction and classification.","authors":"S V Hemanth, Saravanan Alagarsamy","doi":"10.1007/s40200-023-01220-6","DOIUrl":null,"url":null,"abstract":"<p><strong>Objectives: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":15635,"journal":{"name":"Journal of Diabetes and Metabolic Disorders","volume":null,"pages":null},"PeriodicalIF":1.8000,"publicationDate":"2023-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10225400/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Diabetes and Metabolic Disorders","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s40200-023-01220-6","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/6/1 0:00:00","PubModel":"eCollection","JCR":"Q4","JCRName":"ENDOCRINOLOGY & METABOLISM","Score":null,"Total":0}
引用次数: 0
Abstract
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.
期刊介绍:
Journal of Diabetes & Metabolic Disorders is a peer reviewed journal which publishes original clinical and translational articles and reviews in the field of endocrinology and provides a forum of debate of the highest quality on these issues. Topics of interest include, but are not limited to, diabetes, lipid disorders, metabolic disorders, osteoporosis, interdisciplinary practices in endocrinology, cardiovascular and metabolic risk, aging research, obesity, traditional medicine, pychosomatic research, behavioral medicine, ethics and evidence-based practices.As of Jan 2018 the journal is published by Springer as a hybrid journal with no article processing charges. All articles published before 2018 are available free of charge on springerlink.Unofficial 2017 2-year Impact Factor: 1.816.