Diabetic retinopathy detection using developed hybrid cascaded multi-scale DCNN with hybrid heuristic strategy

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dc.contributor.authorTabtaba, Ahlam Asadig Ali
dc.contributor.authorAta, Oğuz
dc.date.accessioned2024-02-20T09:50:10Z
dc.date.available2024-02-20T09:50:10Z
dc.date.issued2024en_US
dc.departmentEnstitüler, Lisansüstü Eğitim Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalıen_US
dc.description.abstractIn recent times, Diabetic Retinopathy (DR) is the most inevitable ailment caused by high blood sugar levels in humans. Conversely, the detection process is accomplished by many learning algorithms. Some models have used the single view of retinal images, it renders unsatisfactory outcomes to forecast the disorder. Due to inadequate of retinal lesion features, the system gradually degrades the performance, and the system has a fewer tendencies to diagnose the disease. On the other hand, the computer-based model is suggested to detect the DR. Nevertheless, these methods are most cost and computationally effective and behinds with feature representation, futile to classify the diseases. To conquer against these shortcomings, a novel DR diagnosis model is proposed using a hybrid heuristic-aided deep learning model with fundus images. The retinal fundus images are collected that are given to the pre-processing stages as scaling, cropping and "Contrast Limited Adaptive Histogram Equalization (CLAHE)". In image augmentation, the high quality image is given for further processing with the help of Generative Adversarial Networks (GANs). Subsequently, the augmented images are fed into the model of a Hybrid cascaded Multi-scale Dilated Convolutional Neural Network (HCMD-CNN), where the Residual Attention Network (RAN) and the MobileNet are integrated to provide promising results for DR detection. Furthermore, the parameters present inside the HCMD-CNN are optimized to achieve higher performance with the help of newly designed Modified Sooty Tern Golden Eagle Optimization (MSTGEO). The implementation results will be analyzed through existing DR detection schemes to ensure the efficiency of the suggested DR detection model.en_US
dc.identifier.citationTabtaba, A. A. A., Ata, O. (2024). Diabetic retinopathy detection using developed hybrid cascaded multi-scale DCNN with hybrid heuristic strategy. Biomedical Signal Processing and Control, 89.en_US
dc.identifier.issn1746-8094
dc.identifier.issn1746-8108
dc.identifier.scopus2-s2.0-85183736173
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://hdl.handle.net/20.500.12939/4576
dc.identifier.volume89en_US
dc.identifier.wosWOS:001127273000001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorTabtaba, Ahlam Asadig Ali
dc.institutionauthorAta, Oğuz
dc.language.isoen
dc.relation.ispartofBiomedical Signal Processing and Control
dc.relation.isversionof10.1016/j.bspc.2023.105718en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - İdari Personel ve Öğrencien_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDiabetic retinopathyen_US
dc.subjectFundus imagesen_US
dc.subjectGenerative adversarial networksen_US
dc.subjectModified sooty tern golden eagle optimizationen_US
dc.subjectHybrid cascaded multi -scale dilated convolutional neural networken_US
dc.subjectResidual attention networken_US
dc.subjectMobileNeten_US
dc.titleDiabetic retinopathy detection using developed hybrid cascaded multi-scale DCNN with hybrid heuristic strategy
dc.typeArticle

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