Demyelinating diseases of the central nervous system (CNS), such as acute disseminated encephalomyelitis (ADEM), multiple sclerosis (MS), and neuromyelitis optica spectrum disorder (NMOSD), disrupt white matter integrity, leading to severe neurological impairments. Early and accurate detection is crucial but remains challenging due to subtle lesion characteristics, overlapping imaging features, and the time-consuming nature of manual segmentation. This study proposes a novel ensemble quantum–based deep learning (DL) framework that integrates Gaussian Gabor filtering for noise suppression and local texture enhancement, a Residual Feed-Forward Absolute Coordinate Vision Transformer (RFF-ACVit) for joint segmentation and feature extraction, and a hybrid Quantum Autoencoder–Quantum Convolutional Variational (QA–QCV) classifier for final diagnosis. The quantum modules leverage parallel quantum state evolution and variational inference to preserve critical spatial–contextual information while reducing dimensionality, thereby improving classification robustness. Interpretability is ensured through Shapley Additive Explanations (SHAP), enabling transparent identification of the most influential MRI features contributing to each prediction. On a real-world CHZU MRI dataset, the proposed model achieves 98.26% Dice score for segmentation and 99.35% classification accuracy, outperforming state-of-the-art CNN, Transformer, and conventional U-Net–based methods by a margin of 2–8%. This demonstrates its potential as an accurate, interpretable, and computationally efficient decision-support tool for CNS demyelinating disease detection.