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Alzheimer Disease Prediction – Deep Learning


Author(s):

Sarandeep Singh , Department of Computer Engineering, Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune, India-411043; M.M Mane, Department of Computer Engineering, Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune, India-411043; S.B Nikam, Department of Computer Engineering, Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune, India-411043; Vibhuti Verma, Department of Computer Engineering, Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune, India-411043; Shivanshu Singh, Department of Computer Engineering, Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune, India-411043

Keywords:

Alzheimer's Disease, Deep Learning, Convolutional Neural Network, Magnetic Resonance Imaging, Mild Demented, Non-Demented

Abstract:

The foundation of this project is predicated upon the core idea to stand a solution to Alzheimer's, which is a progressive disease, where dementia symptoms gradually worsen over several years. Early diagnosis of AD is essential for the progress of more prevailing treatments. The application of deep learning to early detection and automated classification of Alzheimer's disease (AD) has proven to be of immense accuracy. We have used a dataset “Alzheimer's Dataset ( 4 class of Images)” from Kaggle to train and test data A significant accuracy of 92.5 is achieved in which the model performed well as we compared with many other related works and it showed that when dealing with large amount of data like medical data the deep learning approaches can be a better option over the traditional machine learning techniques using MRI(Magnetic Resonance Imaging) scan brain images, we can detect and predict the disease and classify the AD patients whether they have or may not have this deadly disease in future.


Other Details:

Manuscript Id :IJSTEV8I2009
Published in :Volume : 8, Issue : 2
Publication Date: 01/09/2021
Page(s): 24-28
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