Enhancing diabetic retinopathy detection through federated convolutional neural networks: Exploring different stages of progression
Diabetic Retinopathy (DR) is a usual diabetes consequence as well as the world's leading cause of vision loss. Proper identification and treatment are critical for preventing visual loss. This paper presents a comprehensive solution for DR identification utilizing deep learning algorithms. To r...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-05-01
|
| Series: | Alexandria Engineering Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016825001930 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849322053781422080 |
|---|---|
| author | Saad Alanazi Rayan Alanazi |
| author_facet | Saad Alanazi Rayan Alanazi |
| author_sort | Saad Alanazi |
| collection | DOAJ |
| description | Diabetic Retinopathy (DR) is a usual diabetes consequence as well as the world's leading cause of vision loss. Proper identification and treatment are critical for preventing visual loss. This paper presents a comprehensive solution for DR identification utilizing deep learning algorithms. To reduce class imbalance and strengthen this model, we first prepare unbalanced data utilizing the Synthetic Minority Oversampling Technique (SMOTE). In addition, Weiner and median filtration(weinmed) reduce turbulence in retinal images, improving the image quality for future investigations. The VGG was created primarily for segmentation and classification and is well-known for its ability to gather fine visual details. The obtained characteristics are incorporated into several CNN (convolutional neural network) models, which generate discriminatory patterns to determine DR severity levels. Federated learning within this plan allows shared instruction across many medical institutions while protecting patient privacy and security. The combination of SMOTE technology data pre-processing, filtering approaches, VGG-based segmentation, federated learning, and CNN (FedCNN) categorization produces encouraging results, demonstrating the feasibility of scalable and secure DR detection systems. The VGG was created primarily for feature extraction and is well-known for its ability to gather fine visual details. The obtained characteristics are incorporated into several CNN (convolutional neural network) models, which generate discriminatory patterns to determine DR severity levels. Also included in this plan is federated learning, allowing shared instruction across many medical institutions while protecting patient privacy and security. |
| format | Article |
| id | doaj-art-c0156e553dc74c22a32ef47d8d0051dc |
| institution | Kabale University |
| issn | 1110-0168 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj-art-c0156e553dc74c22a32ef47d8d0051dc2025-08-20T03:49:33ZengElsevierAlexandria Engineering Journal1110-01682025-05-0112021522810.1016/j.aej.2025.02.026Enhancing diabetic retinopathy detection through federated convolutional neural networks: Exploring different stages of progressionSaad Alanazi0Rayan Alanazi1Department of Computer Science, College of Computer and Information Sciences, Jouf University, Saudi ArabiaCorresponding author.; Department of Computer Science, College of Computer and Information Sciences, Jouf University, Saudi ArabiaDiabetic Retinopathy (DR) is a usual diabetes consequence as well as the world's leading cause of vision loss. Proper identification and treatment are critical for preventing visual loss. This paper presents a comprehensive solution for DR identification utilizing deep learning algorithms. To reduce class imbalance and strengthen this model, we first prepare unbalanced data utilizing the Synthetic Minority Oversampling Technique (SMOTE). In addition, Weiner and median filtration(weinmed) reduce turbulence in retinal images, improving the image quality for future investigations. The VGG was created primarily for segmentation and classification and is well-known for its ability to gather fine visual details. The obtained characteristics are incorporated into several CNN (convolutional neural network) models, which generate discriminatory patterns to determine DR severity levels. Federated learning within this plan allows shared instruction across many medical institutions while protecting patient privacy and security. The combination of SMOTE technology data pre-processing, filtering approaches, VGG-based segmentation, federated learning, and CNN (FedCNN) categorization produces encouraging results, demonstrating the feasibility of scalable and secure DR detection systems. The VGG was created primarily for feature extraction and is well-known for its ability to gather fine visual details. The obtained characteristics are incorporated into several CNN (convolutional neural network) models, which generate discriminatory patterns to determine DR severity levels. Also included in this plan is federated learning, allowing shared instruction across many medical institutions while protecting patient privacy and security.http://www.sciencedirect.com/science/article/pii/S1110016825001930SMOTEWeinmed FilterVGG19Federated learningConvolutional neural network (CNN) |
| spellingShingle | Saad Alanazi Rayan Alanazi Enhancing diabetic retinopathy detection through federated convolutional neural networks: Exploring different stages of progression Alexandria Engineering Journal SMOTE Weinmed Filter VGG19 Federated learning Convolutional neural network (CNN) |
| title | Enhancing diabetic retinopathy detection through federated convolutional neural networks: Exploring different stages of progression |
| title_full | Enhancing diabetic retinopathy detection through federated convolutional neural networks: Exploring different stages of progression |
| title_fullStr | Enhancing diabetic retinopathy detection through federated convolutional neural networks: Exploring different stages of progression |
| title_full_unstemmed | Enhancing diabetic retinopathy detection through federated convolutional neural networks: Exploring different stages of progression |
| title_short | Enhancing diabetic retinopathy detection through federated convolutional neural networks: Exploring different stages of progression |
| title_sort | enhancing diabetic retinopathy detection through federated convolutional neural networks exploring different stages of progression |
| topic | SMOTE Weinmed Filter VGG19 Federated learning Convolutional neural network (CNN) |
| url | http://www.sciencedirect.com/science/article/pii/S1110016825001930 |
| work_keys_str_mv | AT saadalanazi enhancingdiabeticretinopathydetectionthroughfederatedconvolutionalneuralnetworksexploringdifferentstagesofprogression AT rayanalanazi enhancingdiabeticretinopathydetectionthroughfederatedconvolutionalneuralnetworksexploringdifferentstagesofprogression |