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@article{207235,
        author = {GAGANDEEP B and Dr. GURURAJ T and Dr. Sreenivasa B R},
        title = {Alzheimer’s disease detection using Deep Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {3},
        pages = {69-73},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=207235},
        abstract = {Alzheimer's disease is a progressive brain disorder that damages nerve cells, causing memory loss, impaired thinking, and difficulty performing daily activities. Early detection is crucial for effective treatment and improved patient care, but conventional diagnosis relies on specialists manually analyzing MRI scans and medical reports, which can be time-consuming and subject to interpretation differences. This paper presents an automated system for classifying Alzheimer's disease stages from MRI brain images using a hybrid Convolutional Neural Network (CNN) and Support Vector Machine (SVM) model, where the CNN extracts important image features and the SVM performs the final classification. The system classifies MRI images into four categories: Non-Demented, Very Mild Demented, Mild Demented, and Moderate Demented. To enhance usability, the model is integrated with a Streamlit web application that enables users to upload MRI images and instantly receive the predicted disease stage along with a confidence score. Experimental results demonstrate that the hybrid CNN–SVM model outperforms a standalone CNN, particularly in the early detection of Alzheimer's disease, highlighting the potential of artificial intelligence to provide fast, accurate, and reliable support for clinical diagnosis.},
        keywords = {Alzheimer's Disease, Magnetic Resonance Imaging (MRI), Hybrid CNN-SVM Model, Feature Extraction, Non-Demented, Very Mild Demented, Mild Demented, Moderate Demented},
        month = {August},
        }