Pediatric TSC-Related Epilepsy Classification from Clinical MR Images Using Quantum Neural Network

TL;DR

This paper introduces QResNet, a novel quantum-enhanced deep learning model that improves classification accuracy of pediatric TSC-related epilepsy from MRI images, demonstrating the potential of quantum computing in medical diagnostics.

Contribution

The study presents QResNet, integrating quantum neural network components with CNNs, achieving superior performance over traditional models in TSC MRI classification.

Findings

QResNet outperforms 3D-ResNet in accuracy and AUC metrics.

Quantum neural network integration enhances medical image classification.

The approach demonstrates the potential of quantum computing in medical diagnostics.

Abstract

Tuberous sclerosis complex (TSC) manifests as a multisystem disorder with significant neurological implications. This study addresses the critical need for robust classification models tailored to TSC in pediatric patients, introducing QResNet,a novel deep learning model seamlessly integrating conventional convolutional neural networks with quantum neural networks. The model incorporates a two-layer quantum layer (QL), comprising ZZFeatureMap and Ansatz layers, strategically designed for processing classical data within a quantum framework. A comprehensive evaluation, demonstrates the superior performance of QResNet in TSC MRI image classification compared to conventional 3D-ResNet models. These compelling findings underscore the potential of quantum computing to revolutionize medical imaging and diagnostics.Remarkably, this method surpasses conventional CNNs in accuracy and Area Under the Curve (AUC) metrics with the current dataset. Future research endeavors may focus on exploring the scalability and practical implementation of quantum algorithms in real-world medical imaging scenarios.