Deep Learning Based Dementia Classification and Monitoring Using an Android Application

Abstract

Introduction: Dementia is a long-term neurodegenerative disease that involves cognitive impairment, impacting memory, reasoning, and daily living. Early diagnosis is essential for effective disease management and intervention planning. Conventional diagnosis is based on clinical assessment and interpretation of neuroimages, which are labor-intensive and operator-dependent. The progress in deep learning and image processing offers a faster, more automatic way of dementia classification, allowing for improved monitoring and preventative care planning.

Objectives: The main aims of this research are:To create a deep learning-based system for dementia classification with ResNet-101 for analyzing brain MRI scans.To offer a predictive risk score for clinicians to evaluate the severity of disease and track improvement.To integrate real-time support tools within a mobile app for enhanced caregiving and patient care.To decrease caregiver burden through automated monitoring, early warning, and extended care planning.

Methods: The system proposed works on ResNet-101, a common CNN used because of its ability to extract features to a deep extent. The MRI scans undergo preprocessing in the form of noise removal, normalization, and data augmentation in order to improve performance of the model. Features are extracted and processed to identify the phases of dementia and give a risk score depending upon the severity of the disease. An Android mobile app incorporates real-time monitoring functionality for tracking symptoms, cognitive testing, and caregiver assistance. The model is trained and tested with publicly accessible datasets to provide solid performance and accuracy in detecting dementia.

Results: The system implemented precisely predicts dementia severity levels from MRI scans with high classification accuracy with ResNet-101. The risk model allows for real-time risk assessment, which allows healthcare workers to make effective decisions. Support features of the app by caregivers like location tracking, reminders, and cognitive stimulation greatly improve patient monitoring. Early detection is enhanced, as per initial testing, which would allow for timely medical intervention and better patient outcomes.

Conclusions: The system of dementia diagnosis and monitoring of care based on deep learning exhibits great promise for early disease detection and personalized management of care. By its inclusion of MRI-based analysis and real-time supportive functionality, the app is an end-to-end data-driven solution to dementia care management. Future studies will continue to enhance generalizability of models, introduce further biomarkers, and simplify the app for enhanced clinical utility.