Hybrid deep learning models have strong potential in medical image analysis. They can learn complex patterns from brain MRI data. However, these models often need high computational power. Their training can take a long time. They may also require advanced computers with powerful GPUs. Quantum computing may help reduce this limitation. It can support faster computation and improve model optimisation.

Alzheimer’s disease is a serious and progressive brain disorder. It affects memory, thinking, behaviour, and daily life. The number of affected people worldwide is increasing. This disease creates a major burden for patients, families, caregivers, and healthcare systems. Early diagnosis is very important. It can help doctors plan treatment and care at the right time. However, early-stage Alzheimer’s disease is difficult to detect because brain changes can be very small. For this reason, Alzheimer’s disease should remain an important research topic.

This research aims to develop quantum-assisted hybrid deep learning models for Alzheimer’s disease classification using brain MRI images. The study will use 3-class, 4-class, and 5-class MRI classification tasks. These different class settings will help evaluate the model in simple and complex diagnostic conditions. The proposed model will combine classical deep learning with quantum computing techniques. Classical layers will extract important image features. Quantum layers will support learning and optimization.

The expected outcome is an efficient and accurate classification model for Alzheimer’s disease detection. This research may reduce training time and computational cost. It may also improve MRI-based diagnosis. The study can contribute to faster, smarter, and more reliable medical image analysis for Alzheimer’s disease.