Diabetic retinopathy detection using EADBSC and improved dilated ensemble CNN-based classification

Thomas, Neetha Merin; Jerome, S. Albert

doi:10.1007/s11042-023-16923-4

Multimed Tools Appl

2023

DOI: 10.1007/s11042-023-16923-4

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Diabetic retinopathy detection using EADBSC and improved dilated ensemble CNN-based classification

Neetha Merin Thomas,

S. Albert Jerome

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2024

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Cited by 3 publications

References 48 publications

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Diabetic retinopathy detection using ensembled transfer learning based thrice CNN with SVM classifier

Thomas,

Jerome

2024

Multimed Tools Appl

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Diabetic retinopathy detection using ensembled transfer learning based thrice CNN with SVM classifier

Thomas,

Jerome

2024

Multimed Tools Appl

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Precise detection of diabetic retinopathy using adaptive remora optimization algorithm with deep adversarial approach

Mondal,

Mandal,

Singh

2024

Multimed Tools Appl

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Deep Transfer Learning-Based Automated Diabetic Retinopathy Detection Using Retinal Fundus Images in Remote Areas

Jabbar,

Naseem,

et al. 2024

Int J Comput Intell Syst

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Diabetic retinopathy (DR) significantly burdens ophthalmic healthcare due to its wide prevalence and high diagnostic costs. Especially in remote areas with limited medical access, undetected DR cases are on the rise. Our study introduces an advanced deep transfer learning-based system for real-time DR detection using fundus cameras to address this. This research aims to develop an efficient and timely assistance system for DR patients, empowering them to manage their health better. The proposed system leverages fundus imaging to collect retinal images, which are then transmitted to the processing unit for effective disease severity detection and classification. Comprehensive reports guide subsequent medical actions based on the identified stage. The proposed system achieves real-time DR detection by utilizing deep transfer learning algorithms, specifically VGGNet. The system’s performance is rigorously evaluated, comparing its classification accuracy to previous research outcomes. The experimental results demonstrate the robustness of the proposed system, achieving an impressive 97.6% classification accuracy during the detection phase, surpassing the performance of existing approaches. Implementing the automated system in remote areas has transformed healthcare dynamics, enabling early, cost-effective DR diagnosis for millions. The system also streamlines patient prioritization, facilitating timely interventions for early-stage DR cases.

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Diabetic retinopathy detection using EADBSC and improved dilated ensemble CNN-based classification

Cited by 3 publications

References 48 publications

Diabetic retinopathy detection using ensembled transfer learning based thrice CNN with SVM classifier

Diabetic retinopathy detection using ensembled transfer learning based thrice CNN with SVM classifier

Precise detection of diabetic retinopathy using adaptive remora optimization algorithm with deep adversarial approach

Deep Transfer Learning-Based Automated Diabetic Retinopathy Detection Using Retinal Fundus Images in Remote Areas

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