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@article{168459,
        author = {Kushal Pandey and Nishtha and Ayushman Arya Kumar and Sheenam Naaz},
        title = {Enhancing Alzheimer’s Disease Detection: A Comparative Study Using Deep Learning and Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {5},
        pages = {2198-2202},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=168459},
        abstract = {Alzheimer's disease (AD) is a leading cause of dementia worldwide, characterized by progressive cognitive decline and neurodegeneration. Early and accurate diagnosis is crucial for improving patient outcomes and optimizing treatment. Traditional diagnostic methods, however, are often invasive, time-consuming, and error-prone. Machine learning (ML) and deep learning (DL) techniques have shown promise in the automated detection of Alzheimer's disease, but the optimal way to integrate various data types—imaging, clinical, and demographic—remains underexplored. Furthermore, there is a lack of comprehensive comparative studies assessing the performance of different ML and DL models using publicly available datasets. This research proposes a comprehensive evaluation framework that compares state-of-the-art ML and DL models, and introduces a hybrid model that leverages the strengths of both approaches. By focusing on model effectiveness, interpretability, generalizability, and computational efficiency, this study provides actionable insights for enhancing the automation of Alzheimer’s disease detection.},
        keywords = {Neuroimaging, Deep Learning, MRI and PET scans, Automated Diagnosis, Convolutional Neural Networks (CNN)},
        month = {November},
        }