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@article{159063,
        author = {M.Aishwarya and Pushpa Bista and Mandala Vandana and Pinninti Srija Reddy and Ragottham Krishna Vamshi},
        title = {Multi Class Alzheimer's Disease Detection Using Deep Learning Technique},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {9},
        number = {11},
        pages = {304-307},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=159063},
        abstract = {Alzheimer's disease is the extremely popular cause of dementia that causes memory loss. People who have Alzheimer's disease suffer from a disorder in neurodegenerative which leads to loss in many brain functions. Nowadays researchers prove that early diagnosis of the disease is the most crucial aspect to enhance the care of patients’ lives and enhance treatment. Traditional approaches for diagnosis of Alzheimer’s disease (AD) suffers from long time with lack both efficiency and the time it takes for learning and training. Lately, deep-learning-based approaches have been considered for the classification of neuroimaging data correlated to AD. In this paper, we study the use of the Convolutional Neural Networks (CNN) in AD early detection, VGG-16 trained on our datasets is used to make feature extractions for the classification process. Experimental work explains the effectiveness of the proposed approach.},
        keywords = {Alzheimer’s disease, CNN(convolution neural network) , VGG-16},
        month = {},
        }