Multi-Class Neurological Disorder Prediction with Tensor Network Feature Engineering

TL;DR

This paper introduces a quantum-inspired tensor network approach for classifying neurological disorders from MRI images, demonstrating robustness and competitive accuracy on a large clinical dataset.

Contribution

It presents a novel classical implementation of quantum neural network concepts using PARAFAC tensor decompositions for medical image classification.

Findings

Achieved strong validation performance across multiple tensor ranks.

Model demonstrated robustness to tensor network expressivity.

Competitive accuracy compared to recent classical methods.

Abstract

Accurate diagnosis of neurological disorders is contingent upon advanced imaging modalities such as Magnetic Resonance Imaging (MRI), which commonly utilize sparse imaging techniques to reconstruct images from limited data, thus reducing storage and acquisition time. However, challenges remain in managing noise and preserving critical diagnostic features for effective analysis. In this study, an ensemble classifier is enriched with PARAFAC CP tensor decompositions, drawing mathematical inspiration from quantum neural network architectures but implemented entirely classically. The model was evaluated on a large, balanced clinical dataset comprising 55,160 images across 8 diagnostic categories, employing both higher and lower PARAFAC rank configurations. Evaluated through 5-fold nested stratified cross-validation, both configurations achieved strong validation performance, demonstrating robustness to tensor network expressivity. Additionally, the proposed model achieved competitive performance relative to recent classical approaches, further underscoring the potential of quantum-inspired classical frameworks to enhance medical image analysis and support reliable clinical diagnosis. Future work will explore the integration of advanced encoding schemes, deployment on real quantum hardware, and the use of more diverse neurological datasets.