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@article{174162,
        author = {P.NithinReddy and K.srihari and B.sainaik and P.pavansai and S.suneetha},
        title = {Portable Alzheimer's disease Detection System Using Raspberry Pi 5 and Pre-Trained Deep Learning Models},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {3059-3065},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174162},
        abstract = {Alzheimer's disease is a progressive neurodegenerative disorder that affects memory, thinking, and behavior. It is the most common cause of dementia, primarily affecting older adults. Early detection of Alzheimer's is crucial because it allows for earlier intervention, which may slow the progression of symptoms and improve patient outcomes.This project proposes a deep learning-based system to detect Alzheimer's disease using structural MRI scans. By developing a convolutional neural network (CNN), the system will automatically analyze MRI images to identify subtle patterns in brain structure that may indicate early stages of Alzheimer's. The system will be implemented on a Raspberry Pi 5 with a user-friendly interface, allowing clinicians and researchers to quickly identify signs of Alzheimer's in MRI scans. The goal is to provide an efficient tool for early diagnosis, contributing to better management of the disease.},
        keywords = {Alzheimer’s disease, Deep Learning, Structural MRI, Raspberry Pi 5, Convolutional Neural Network (CNN), Neurodegenerative disorder.},
        month = {March},
        }