CENTRALIZED SOLUTION FOR ALZHEIMER’S DETECTION AND CLASSIFICATION

Abstract

Alzheimer’s disease is a progressive neurological disorder that significantly impacts memory, cognitive abilities, daily activities, and overall quality of life, while also increasing the responsibilities placed on caregivers. Despite the availability of various diagnostic and support technologies, many existing solutions operate independently, making patient monitoring and care management fragmented and less effective. This paper proposes a centralized Alzheimer’s care platform that combines artificial intelligence-based MRI analysis with intelligent object tracking and caregiver support features within a unified environment. The system employs deep learning techniques to analyze MRI scans and assist in identifying different stages associated with cognitive decline, providing an additional tool for early awareness and monitoring. To support day-to-day living, a computer vision module continuously observes object interactions, recognizes hand movements, records item locations, and generates visual activity summaries that help users and caregivers locate frequently misplaced belongings. The platform further incorporates an accessible web interface, secure data management mechanisms, caregiver monitoring dashboards, and a personalized recommendation component that delivers useful insights based on observed behavioral patterns. Comprehensive functional evaluation confirms the effectiveness of the MRI processing workflow, item tracking operations, activity visualization generation, and caregiver notification mechanisms. Rather than serving as a replacement for professional medical assessment, the proposed framework is designed as a supportive assistance system that enhances patient care, improves caregiver awareness, and promotes better management of daily challenges associated with Alzheimer’s disease. The integration of diagnostic support, behavioral monitoring, and intelligent recommendations within a single platform demonstrates the potential of combining artificial intelligence and assistive technologies to improve the quality of life of individuals affected by cognitive disorders. Future enhancements may include clinical-scale validation, multimodal healthcare data integration, privacy-focused deployment strategies, advanced predictive analytics, and extensive usability studies involving patients, caregivers, and healthcare professionals.