Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{197298,
        author = {HARISH KUMAR K G and BAVEN AR K GOVIN and SANTHOSH JR and G V SHRICHANDRAN},
        title = {Early Detection of Alzheimer’s Disease Using Deep Learning on Neuroimaging Data},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {6691-6695},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=197298},
        abstract = {Alzheimer’s Disease (AD) is a progressive neurological condition that gradually affects memory and cognitive abilities, impacting millions of individuals worldwide. Detecting the disease at an early stage is crucial, as it allows timely medical intervention and better management of its progression before severe damage occurs. Neuroimaging methods such as Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) play an important role in identifying early structural and functional changes in the brain. However, conventional diagnostic approaches rely heavily on manual analysis by medical experts, which can be time-consuming and may lead to variations in interpretation.
To overcome these challenges, this study presents a deep learning-based approach for automated detection of Alzheimer’s Disease using neuroimaging data. The proposed framework employs Convolutional Neural Networks (CNNs) along with hybrid learning techniques to automatically learn relevant features from brain scans without the need for manual feature extraction. This approach enhances classification performance across different stages of the disease and provides a consistent and scalable tool that can assist clinicians in early diagnosis.},
        keywords = {Alzheimer’s Disease, Deep Learning, Convolutional Neural Networks (CNN), MRI, Early Diagnosis},
        month = {April},
        }