Abstract:Diabetic Retinopathy (DR) is a common complication of diabetes that can lead to blindness of people. Detecting DR at the earliest stage is essential to prevent irreversible eye damage. Microaneurysm dots are the first signs of DR. As the dots are tiny and of low contrast, detecting mild DR is a very challenging task. Federated learning (FL) preserves data privacy, which is a major concern for medical image processing. FL is a collaborative learning method, which shares only the model parameters with a server, without sharing the patient data to a central server. Inspired by classical FL, we propose a federated learning-based quantum neural network (federated QNN) for this task. We implemented the models with limited samples and few learnable parameters from the E-ophtha and Retina MNIST datasets. The crossevaluation efficiency of the proposed federated quantum neural network system for privacy-preserving early detection of diabetic retinopathy (FQPDR) in Kaggle dataset images indicates the robustness of the light weight learning models. FQPDR performances are inspiring while considering existing non-FL and FL methods.
| Subjects: | Image and Video Processing (eess.IV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.08324 [eess.IV] |
| (or arXiv:2605.08324v1 [eess.IV] for this version) | |
| https://doi.org/10.48550/arXiv.2605.08324 arXiv-issued DOI via DataCite (pending registration) |
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| Journal reference: | Evolutionary Intelligence (17) 4047 4068 (2024) |
| Related DOI: | https://doi.org/10.1007/s12065-024-00971-2
DOI(s) linking to related resources |
Submission history
From: Mahua Nandy Pal [view email]
[v1]
Fri, 8 May 2026 16:49:27 UTC (6,278 KB)