Title: A machine learning approach to assist prediction of Alzheimer's disease with convolutional neural network

Authors: Subhasish Mohapatra; Suneeta Satpathy; Bijay Kumar Paikaray

Addresses: Department of CSE, Adamas University, Kolkata, India ' Faculty in Emerging Technologies, Sri Sri University, Cuttack, India ' School of Information and Communication Technology, Medhavi Skills University, Sikkim, India

Abstract: Alzheimer's disease (AD) is an advanced form of dementia in which the brain's envelope contracts and gradually exhales. The disease impairs a person's ability to think and impairs social functioning. Human behaviour patterns change dramatically. An early symptom of the disorder is an inability to recall recent events or to hold a conversation. The current work seeks to analyse structural changes in brain images collected from different brain lobes of individuals suffering from AD. In addition, we are trying to decipher deep learning (DL) techniques to study the properties of brain images and use convolutional neural networks (CNNs) to predict early AD. Early prediction of such diseases is critical in saving lives and can lead to premature treatment and medical costs.

Keywords: Alzheimer's disease; AD; artificial intelligence; AI; convolutional neural network; CNN; image analysis.

DOI: 10.1504/IJBRA.2023.132632

International Journal of Bioinformatics Research and Applications, 2023 Vol.19 No.2, pp.141 - 150

Received: 17 Jan 2023
Accepted: 05 May 2023
Published online: 31 Jul 2023 *

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