DR-FEDPAM: Detection of Diabetic Retinopathy using Federated Proximal Averaging Model
Abstract
Diabetic retinopathy (DR) is an eye condition caused by damage to the blood vessels of the retina due to high blood sugar levels, commonly associated with diabetes. Without proper treatment, it can lead to visual impairment or blindness. Traditional machine learning (ML) approaches for detecting Diabetic retinopathy rely on centralized data aggregation, which raises significant privacy concerns and often encounters regulatory challenges. To address these issues, the DR-FEDPAM model is proposed for the detection of diabetic retinopathy. Initially, the images are preprocessed using a Median Filter (MeF) and Gaussian Star Filter (GaSF) to reduce noise and enhance image quality. The preprocessed images are then input into a federated proximal model. Federated Learning (FL) enables multiple local models to train on distributed devices without sharing raw data. After the local models process the data, their parameters are aggregated through a Global Federated Averaging (GFA) model. This global model combines the parameters from all local models to produce a unified model that classifies each image as either normal or diabetic retinopathy. The model’s performance is evaluated using precision (PR), F1-score (F1), specificity (SP), recall (RE), and accuracy (AC). The DR-FEDPAM achieves a balanced trade-off with 7.8 million parameters, 1.7 FLOPs, and an average inference time of 13.9 ms. The model improves overall accuracy by 5.44%, 1.89%, and 4.43% compared to AlexNet, ResNet, and APSO, respectively. Experimental results show that the proposed method achieves an accuracy of 98.36% in detecting DR
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References
R. Vignesh, N. Muthukumaran and M. P. Austin, “Hybrid ResNet with bidirectional LSTM for eye disease classification with evaluation optimizers techniques,” In 2023 International Conference on Inventive Computation Technologies (ICICT), pp. 1624-1630, 2023, doi: 10.1109/icict57646.2023.10134223
R. Priya and P. Aruna, “Diagnosis of diabetic retinopathy using machine learning techniques,” ICTACT Journal on soft computing, vol. 3, no. 4, pp. 563-575, 2013, doi: 10.21917/ijsc.2013.0083
H.Y. Tsao, P.Y. Chan and E.C.Y. Su, “Predicting diabetic retinopathy and identifying interpretable biomedical features using machine learning algorithms,” BMC bioinformatics, vol. 19, pp. 111-121, 2018, doi: 10.1186/s12859-018-2277-0
D. Das, S.K. Biswas and S. Bandyopadhyay, “A critical review on diagnosis of diabetic retinopathy using machine learning and deep learning,” Multimedia Tools and Applications, vol. 81, no. 18, pp. 25613-25655, 2022, doi: 10.1007/s11042-022-12642-4
M.J. Hossain, M. Al‐Mamun and M.R. Islam, "Diabetes mellitus, the fastest growing global public health concern: Early detection should be focused," Health Science Reports, vol. 7, no. 3, pp. e2004, 2024.
B.S. Mankar and N. Rout, “Automatic detection of diabetic retinopathy using morphological operation and machine learning,” ABHIYANTRIKI Int. J. Eng. Technol, vol. 3, no. 5, pp. 12-19, 2016, doi: 10.1007/s11042-023-16813-9
F. Jeribi, T. Nazir, M. Nawaz, A. Javed, M. Alhameed and A. Tahir, “Recognition of diabetic retinopathy and macular edema using deep learning,” Medical & Biological Engineering & Computing, pp. 1-15, 2024, doi: 10.1007/s11517-024-03105-z
J. Angel Sajani and A. Ahilan, “Classification of brain disease using deep learning with multi-modality images,” J. Intell. Fuzzy Syst. (Preprint), pp. 1-11, 2023, doi: 10.3233/jifs-230090
A. Ahilan, J. Angel Sajani, A. Jasmine Gnana Malar, B. Muthu Kumar, "Machine Learning-Based Brain Disease Classification Using EEG and MEG Signals," In International Conference on Frontiers of Intelligent Computing: Theory and Applications, pp. 487-498, 2023, doi: 10.1007/978-981-99-6702-5_40
B. Pradheep T Rajan and N. Muthukumaran, “Optimized CNN and adaptive RBFNN for channel estimation and hybrid precoding approaches for multi user millimeter wave massive MIMO,” International Journal of Electronics, Published online, July 2024, doi: 10.1080/00207217.2024.2372060
R. Bala, A. Sharma and N. Goel, "Comparative Analysis of Diabetic Retinopathy Classification Approaches Using Machine Learning and Deep Learning Techniques," Archives of Computational Methods in Engineering, vol. 31, no. 2, 2024, doi: 10.1007/s11831-023-10002-5
Y. Suo, Z. He and Y. Liu, "Deep learning CS-ResNet-101 model for diabetic retinopathy classification," Biomedical Signal Processing and Control, vol. 97, pp. 106661, 2024, doi: 10.1016/j.bspc.2024.106661
I. Arias-Serrano, P.A. Velásquez-López, L.N. Avila-Briones, F.C. Laurido-Mora, F. Villalba-Meneses, A. Tirado-Espin, J. Cruz-Varela and D. Almeida-Galárraga, "Artificial intelligence based glaucoma and diabetic retinopathy detection using MATLAB—Retrained AlexNet convolutional neural network," F1000Research, vol. 12, pp. 14, 2024, doi: 10.12688/f1000research.122288.2
T.D. Nguyen, D.T. Le, J. Bum, S. Kim, S.J. Song and H. Choo, "Retinal disease diagnosis using deep learning on ultra-wide-field fundus images," Diagnostics, vol. 14, no. 1, pp. 105, 2024, doi: 10.3390/diagnostics14010105
B. Shi, X. Zhang, Z. Wang, J. Song, J. Han, Z. Zhang and T.T. Toe, "GoogLeNet-based Diabetic-retinopathy-detection," In 2022 14th International Conference on Advanced Computational Intelligence (ICACI), pp. 246-249, 2022, doi: 10.1109/icaci55529.2022.9837677
Cheepurupalli Raghuram, V. S. R. K. Raju Dandu and B. Jaison, “HYBRIDIZATION OF DILATED CNN WITH ATTENTION LINK NET FOR BRAIN CANCER CLASSIFICATION,” International Journal of Data Science and Artificial Intelligence, vol. 02, no.02, pp. 35-42, 2024.
Jhansi Bharathi Madavarapu, “CAB-IDS: IoT-based Intrusion Detection using Bacteria Foraging Optimized BiGRU-CNN Network,” International Journal of Computer and Engineering Optimization, vol. 01, no. 02, pp. 63-68, 2023.
D. Igrec, G. Golobich, and M. Urbanija, Design and Optimization of Affordable Embedded Medical Devices for Resource-Constrained Settings, 2025.
G. Kaissis, A. Ziller, J. Passerat-Palmbach, T. Ryffel, D. Usynin, A. Trask, I. Lima Jr, J. Mancuso, F. Jungmann, M.M. Steinborn and A. Saleh, "End-to-end privacy preserving deep learning on multi-institutional medical imaging," Nature Machine Intelligence, 3(6), pp. 473-484, 2021, doi: 10.1038/s42256-021-00337-8
C. Lahmar and A. Idri, “Classifying Diabetic Retinopathy using CNN and Machine Learning,” In Bioimaging, pp. 52-62, 2022, doi: 10.5220/0010851500003123
S. Dasari, B. Poonguzhali and M.S. Rayudu, “An efficient machine learning approach for classification of diabetic retinopathy stages,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 30, no. 1, pp. 81-88, 2023, doi: 10.11591/ijeecs.v30.i1.pp81-88
A.M. Fayyaz, M.I. Sharif, S. Azam, A. Karim and J. El-Den, “Analysis of diabetic retinopathy (DR) based on the deep learning,” Information, vol. 14, no. 1, pp. 30, 2023, doi: 10.3390/info14010030
S. Basheer and R.E. Varghese, “Estimation of diabetic retinopathy using deep learning,” In AIP Conference Proceedings, vol. 3059, no. 1, 2024, doi: 10.1063/5.0194492
V. Sapra, L. Sapra, A. Bhardwaj, A. Almogren, S. Bharany, A. Ur Rehman and K. Ouahada, “Diabetic Retinopathy Detection Using Deep Learning with Optimized Feature Selection,” Traitement du Signal, pp. 41, no. 2, 2024, doi: 10.18280/ts.410219
A. Jabbar, H.B. Liaqat, A. Akram, M.U. Sana, I.D. Azpíroz, I.D.L.T. Diez and I. Ashraf, “A Lesion-Based Diabetic Retinopathy Detection Through Hybrid Deep Learning Model,” IEEE Access, 2024, doi: 10.1109/access.2024.3373467
R. Chaudhuri and S. Deb, “Precise lesion analysis to detect diabetic retinopathy using Generative Adversarial Network (GAN) and Mask-RCNN,” Procedia Computer Science, vol. 235, pp. 520-529, 2024, doi: 10.1016/j.procs.2024.04.051
A.M. Mutawa, K. Al-Sabti, S. Raizada and S. Sruthi, “A Deep Learning Model for Detecting Diabetic Retinopathy Stages with Discrete Wavelet Transform,” Applied Sciences, vol. 14, no. 11, pp. 4428, 2024, doi: 10.3390/app14114428
D.A. Da Rocha, F.M.F. Ferreira, and Z.M.A. Peixoto, "Diabetic retinopathy classification using VGG16 neural network," Research on Biomedical Engineering, vol. 38, no. 2, pp.761-772, 2022, doi: 10.1007/s42600-022-00200-8
B. Ramasubramanian and S. Selvaperumal, "A comprehensive review on various preprocessing methods in detecting diabetic retinopathy," In 2016 international conference on communication and signal processing (ICCSP), pp. 0642-0646, 2016, doi: 10.1109/iccsp.2016.7754220
H. Avcı and J. Karakaya, "A novel medical image enhancement algorithm for breast cancer detection on mammography images using machine learning," Diagnostics, vol. 13, no. 3, pp. 348, 2023, doi: 10.3390/diagnostics13030348
K. Malathi and R. Nedunchelian, "An Efficient Method to Detect Diabetic Retinopathy Using Gaussian-Bilateral and Haar Filters with Threshold Based Image Segmentation," Research Journal of Applied Sciences, Engineering and Technology, vol. 8, no. 11, pp.1389-1395, 2014, doi: 10.19026/rjaset.8.1112
K. Kumar and B.T. Chakkaravarthy, "Contrast Enhancement and Noise Reduction Through Synthesizing Gaussian Filter, CLAHE and HE Algorithm for Image Preprocessing," In 2025 International Conference on Emerging Technologies in Engineering Applications (ICETEA), pp. 1-5, 2025, doi: 10.1109/icetea64585.2025.11100055
A. APPATHURAI, A. S. I. TINU and M. NARAYANAPERUMAL, “MEG AND PET IMAGES-BASED BRAIN TUMOR DETECTION USING KAPUR’S OTSU SEGMENTATION AND SOOTY-OPTIMIZED MOBILENET CLASSIFICATION,” REVUE ROUMAINE DES SCIENCES TECHNIQUES—SÉRIE ÉLECTROTECHNIQUE ET ÉNERGÉTIQUE, vol. 69, no. 3, pp. 363-368, 2024, doi: 10.59277/rrst-ee.2024.69.3.18.
T. Zhou, Z. Lin, J. Zhang, and D.H. Tsang, "Understanding and improving model averaging in federated learning on heterogeneous data," IEEE Transactions on Mobile Computing, vol. 23, no. 12, pp.12131-12145, 2024, doi: 10.1109/tmc.2024.3406554
A. Bilal, M. Shafiq, W.J. Obidallah, Y.A. Alduraywish, A. Tahir and H. Long, "Quantum chimp-enanced SqueezeNet for precise diabetic retinopathy classification," Scientific Reports, vol. 15, no. 1, pp. 12890, 2025, doi: 10.1038/s41598-025-97686-w
R.S. Rajkumar and A.G. Selvarani, "Diabetic Retinopathy Diagnosis Using ResNet with Fuzzy Rough C-Means Clustering," Computer Systems Science & Engineering, vol. 42, no. 2, 2022, doi: 10.32604/csse.2022.021909
S. Balaji and B. Karthik, "A Context for Effective Prediction and Classification of Diabetic Retinopathy Disease using Deep Ensemble AlexNet & LeNet Classifier," In 2024 11th International Conference on Computing for Sustainable Global Development (INDIACom), pp. 414-421, 2024, doi: 10.23919/indiacom61295.2024.10498405
S. Chaurasiya, N. Singh and A. Singh, "Mobile-based diabetic retinopathy detection and classification using TinyML," In Advances in Science, Engineering and Technology, pp. 398-404, 2025, doi: 10.1201/9781003641544-73
B. Shi, X. Zhang, Z. Wang, J. Song, J. Han, Z. Zhang and T.T. Toe, "GoogLeNet-based Diabetic-retinopathy-detection," In 2022 14th International Conference on Advanced Computational Intelligence (ICACI), pp. 246-249, 2022, doi: 10.1109/icaci55529.2022.9837677
M.T. Hagos and S. Kant, "Transfer learning based detection of diabetic retinopathy from small dataset," arXiv preprint arXiv:1905.07203, 2019.
P. Porwal, S. Pachade, R. Kamble, M. Kokare, G. Deshmukh, V. Sahasrabuddhe and F. Meriaudeau, "Indian diabetic retinopathy image dataset (IDRiD): a database for diabetic retinopathy screening research," Data, vol. 3, no. 3, pp. 25, 2018, doi: 10.3390/data3030025
L. Giancardo, T.P. Karnowski, K.W. Tobin, F. Meriaudeau and E. Chaum, "Validation of microaneurysm-based diabetic retinopathy screening across retina fundus datasets," In Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems, pp. 125-130, 2013, doi: 10.1109/cbms.2013.6627776
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