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@article{173425,
        author = {Mohan Yerra and Swathi katyayani and K V V Varshith and Hiteesh katikala and P.Adithya Siva Shankar},
        title = {An MRI-Based Approach for Early Detection of Alzheimer’s Disease with VGG16 Feature Extractor},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {1180-1185},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=173425},
        abstract = {Alzheimer’s disease (AD) is a neurodegenerative disorder and the most common cause of dementia, primarily affecting individuals over 60. Early diagnosis remains challenging due to unreliable detection methods, leading to disease progression before intervention. Identifying AD stages—very mild, mild, and moderate dementia—is crucial to slowing advancement and improving patient care. This study employs deep learning (DL) techniques for AD detection using MRI-based analysis. A neural network classifier with a VGG16 feature extractor was applied to two MRI datasets containing 6400 and 6330 images to classify AD stages. The proposed model achieved an accuracy of 98.73%, with a precision, recall, and F1-score of 0.99, demonstrating superior performance compared to previous methods. The results highlight the effectiveness of DL-based approaches in enhancing AD diagnosis accuracy and efficiency. This research underscores the potential of AI-driven diagnostic tools in medical imaging, paving the way for scalable, automated early detection solutions. By advancing computational neurology and early intervention strategies, this study contributes to improved patient outcomes and the broader field of medical AI applications.},
        keywords = {Alzheimer’s disease, deep learning, MRI analysis, VGG16, early diagnosis, neural network, medical imaging},
        month = {March},
        }