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@article{197537,
        author = {BIRADAR ARJUN and CHEPAROLU SAROJ KUMAR and NAKSHATRAM SANKEERTH and VADDE SREERAMUDU and M.KAVYA},
        title = {Alzheimer classification using Deep Learing},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {5764-5772},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=197537},
        abstract = {Alzheimer’s disease is a progressive neurological disorder that causes cognitive decline and memory loss, making early detection and stage classification essential for effective treatment planning. To address this challenge, we developed a deep learning–based system for Alzheimer’s stage classification using brain MRI images. The system leverages Convolutional Neural Networks (CNNs) to automatically extract spatial and structural features from MRI scans and classify patients into four categories — non-demented, very mild, mild, and moderate.    
We curated and utilized a Kaggle MRI dataset of Alzheimer’s patients, applying preprocessing and WWW.jet mapping techniques to enhance image quality and improve feature representation. The CNN model was trained and evaluated on this dataset to achieve accurate classification performance.},
        keywords = {},
        month = {April},
        }