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

Tuberous sclerosis complex (TSC) presents as a multisystem disorder with profound neurological impacts. Our study introduces Quantum-Residual Neural Network (QR-Net), a novel quantum neural network model that innovatively combines conventional residual convolutional neural networks (CNNs) with quantum layers for pediatric TSC classification. QR-Net incorporates a dual-layer quantum module, with the first layer transforming classical inputs into quantum states for enhanced data representation, and the second layer utilizing quantum entanglement and qubit interactions to process these states. A thorough evaluation reveals QR-Net’s superior efficacy in classifying TSC MRI images, outperforming traditional 3D-ResNet34 models. These results highlight the transformative potential of quantum computing in medical imaging and diagnostics, with QR-Net achieving higher accuracy and Area Under the Curve (AUC) metrics than conventional CNNs using the current dataset. Future research should explore the scalability and practical application of quantum algorithms in real-world medical imaging.