Alzheimer’s Disease Detection in MRI images using Deep Convolutional Neural Network Model

S. Naganandhini; P. Shanmugavadivu

doi:10.4108/eetpht.10.6435

Authors

S. Naganandhini Arulmigu Palaniandavar Arts and Science College For Women
P. Shanmugavadivu Gandhigram Rural Institute

DOI:

https://doi.org/10.4108/eetpht.10.6435

Keywords:

Alzheimer’s disease detection, MIRIAD datasets, Confusion Matrix, CNN architecture, ReLu, Dropout, Normal and Abnormal MRI images

Abstract

Alzheimer's disease (AD) is a neurodegenerative disease that affects cognitive abilities (thinking and memory etc) primarily among the elderly, due to which collective cognitive skills deteriorate, ultimately leading to death. Early detection of Alzheimer's disease is crucial for determining appropriate therapeutic options. This research investigates the use of a Deep Convolutional Neural Network (CNN) for detecting Alzheimer's disease. Due to similar brain patterns and pixel intensities, CNN demonstrates promising results in diagnosing AD through automated feature extraction and characterization. Deep Learning algorithms are designed to perform automated feature extraction and categorization of input image datasets. In this study, a two-way classifier categorizes each image as either Healthy Control (HC) or Alzheimer's disease (AD). Experiments were carried out with the MIRIAD dataset, and the accuracy of disease classification into binary categories was evaluated. The recorded results of CNN with 4- and 5 -layer architectures confirms the effectiveness of the proposed method for AD detection.

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Naganandhini, Shanmugavadivu, P., Kanimozhi, & Kavitha, M. S. (2021, September). Data imputation of brain MRI features with enhanced multinomial logistic regression for Alzheimer's disease classification. In Proceedings of the 6th International Conference on Sustainable Information Engineering and Technology (pp. 339-347). DOI: https://doi.org/10.1145/3479645.3479676