FQPDR: Federated Quantum Neural Network for Privacy-preserving Early Detection of Diabetic Retinopathy

TL;DR

This paper introduces FQPDR, a federated quantum neural network that enables privacy-preserving early detection of diabetic retinopathy using limited data and lightweight models.

Contribution

It proposes a novel federated quantum neural network approach tailored for privacy-preserving medical image analysis, demonstrating effectiveness on diabetic retinopathy datasets.

Findings

FQPDR shows robust performance on Kaggle diabetic retinopathy images.

The model performs well with limited samples and few parameters.

FQPDR outperforms existing non-FL and FL methods in early DR detection.

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.