Early-stage Alzheimer’s Detection Using Flexible Representation of Quantum Imaging Technique

This paper presents a novel methodology for the early detection of Alzheimer’s disease through MRI brain scans, utilizing quantum image processing. This approach utilizes the Flexible Representation of Quantum Images (FRQI) algorithm to encode, process, and analyze images for early-stage Alzheimer’s detection by identifying subtle patterns indicative of the disease. Quantum encoding, central to this methodology, transforms pixel intensities into quantum states, enabling efficient data manipulation and storage. The FRQI algorithm associates each pixel’s intensity with a quantum angle, encoding the entire image as a superposition of quantum states. This encoding allows for compact and efficient representation of high-dimensional image data, facilitating advanced quantum operations. Fidelity measures, including state vector fidelity and shot noise fidelity, are employed to assess the similarity between test and reference images. Results indicate that quantum metrics significantly outperform classical techniques in terms of reliability and consistency. Quantum metrics exhibit minimal overlap between similar and different images, ensuring accurate classification. This methodology aims to enhance the sensitivity and precision of early Alzheimer’s disease detection, offering a promising approach for advanced medical image analysis.