Identification of Alzheimer's disease using Convolutional Neural Network

Cedric Obundaa Nnah, Yu-Dong Zhang

DOI: https://doi.org/10.51662/jiae.v4i1.144

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Abstract


Alzheimer's disease (AD) as a brain disease has caused a progressive, devastating effect on the memory and general mental and physical coordination of victims. The impact on victims is irreversible, and the cause has yet to be identified. The treatment at full-blown can be difficult, but it could be properly managed in the early phase. Hence, there is a need for an efficient and effective early diagnosis. Machine learning techniques have proved to be successful in image classification. It was on this premise that this paper adopted a machine learning approach. The approach used a convolutional neural network with transfer learning to classify structural Magnetic Resonance Images (sMRI) into a multi-classification of 3 classes. The classes were Normal Cognitive (NC), Mild Cognitive Impairment (MCI) and Alzheimer's Disease (AD). K-fold cross validation was employed to validate the test set. The sMRI subjects included 97 NC, 57 MCI, and 24 AD patients. The proposed method achieved an overall accuracy of 94% on classification based on the multiclass classification.


Keywords


Alzheimer's disease; Convolutional Neural Network; Magnetic Resonance Images (sMRI); Mild Cognitive Impairment; Normal Cognitive;

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Journal of Integrated and Advanced Engineering (JIAE),
Published by:
Asosiasi Staf Akademik Perguruan Tinggi Seluruh Indonesia (ASASI):http://asasi.id/

p-ISSN: 2774-602X
e-ISSN: 2774-6038
Journal URL: https://asasijournal.id/index.php/jiae/
Journal DOI: 10.51662/jiae

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