QViSTA: A Novel Quantum Vision Transformer for Early Multi-Stage Alzheimer’s Diagnosis Using Optimized Variational Quantum Circuits
Abstract: Magnetic resonance imaging (MRI) is widely used by neurologists to detect brain
abnormalities such as strokes, tumors, and various forms of dementia, including
Alzheimer’s disease. However, accurately diagnosing the different stages of
Alzheimer’s disease remains a challenge, with nearly one in five patients misdiagnosed
due to symptom overlap with other conditions. This paper introduces
QViSTA, a novel hybrid quantum vision transformer (QViT) model that exploits
quantum parallelism to improve early diagnosis and differentiation of Alzheimer’s
disease stages. By integrating quantum variational circuits (VQCs) with vision
transformers (ViTs), QViSTA addresses the data scalability and computational
efficiency limitations of classical machine learning models. Using a balanced,
multi-class dataset of 40,000 MRI images, QViSTA achieved a validation area
under the receiver operating characteristic (AUC) of 87.86% and a test AUC of
86.67%, closely matching the performance of a benchmarked classical ViT while
reducing feature space by 3.18%. Early and accurate detection of Alzheimer’s
disease is critical, as it allows for timely interventions that can significantly improve
the quality of life for patients and their caregivers. As more hospitals adopt AI
for biomedical imaging, QViSTA’s innovative approach could dramatically reduce
misdiagnosis rates, improve patient outcomes, and reduce costs.
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