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.

翻译：神经系统疾病的准确诊断依赖于先进成像技术，如磁共振成像（MRI），这类技术通常采用稀疏成像方法从有限数据中重建图像，从而减少存储空间和采集时间。然而，在噪声抑制和关键诊断特征保留以进行有效分析方面仍面临挑战。本研究构建了一种集成分类器，通过引入PARAFAC CP张量分解增强模型性能，其数学灵感源自量子神经网络架构，但完全基于经典计算实现。该模型在包含55,160张图像、涵盖8个诊断类别的大规模平衡临床数据集上进行了评估，并采用了高、低两种PARAFAC秩配置。通过五折嵌套分层交叉验证，两种配置均取得了优异的验证性能，展现出对张量网络表达能力的鲁棒性。此外，所提模型相较于近期经典方法展现出竞争性性能，进一步凸显了量子启发式经典框架在增强医学图像分析和支持可靠临床诊断中的潜力。未来工作将探索高级编码方案的集成、在真实量子硬件上的部署，以及更广泛神经系统数据集的运用。