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@article{176714,
        author = {Ashutosh Pardhan and Aryan Shandhilya and Sarthak Dwivedi and Vaibhav Tyagi and Atin Joshi},
        title = {Progressing Alzheimer’s Diagnosis with Ensemble CNN Model},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {5840-5847},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=176714},
        abstract = {This paper proposes an Alzheimer’s disease prediction model using CNN, implemented on a system with AMD Radeon Vega 8 Graphics to improve computational traceability. The research is encouraging because the implicates of the model touched the 97% accuracy of the disease prediction by MRI images of patients. The integration of the Vega 8 GPU allows the system to process large datasets and perform computations at a significantly faster rate, thereby reducing training time. The MRI dataset used in this study is highly imbalanced, with four classes: Non-Demented or Non-D, Very Mild Dementia or V.M.D, Mild Dementia or M.D, Moderate Demented or M.D.A. CNN was chosen for its capability in highlight extraction and learning from MRI pictures, without requiring human interference. The show was run with parallel and multiclass datasets, and IT got 97% exactness. The systematic analysis of the test shows that the system can diagnose Alzheimer’s right at the initial stage of the disease development. This approach shows how CNNs with the aid of AMD Radeon Vega 8 Graphics perform well in automating Alzheimer’s diagnosis while breaking the barriers associated with manual feature extraction and orthodox dependence on experts.},
        keywords = {Alzheimer’s Disease Prediction, MRI Image Analysis, Adaptive Synthetic Oversampling, Neuroimaging, Early Diagnosis, Convolutional Neural Network.},
        month = {April},
        }