Transfer Learning for Alzheimer's Disease Diagnosis using EfficientNet-B0 Convolutional Neural Network

Authors

  • Wong Pui Ching Malaysia–Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, 54100 Kuala Lumpur, Malaysia
  • Shahrum Shah Abdullah Malaysia–Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, 54100 Kuala Lumpur, Malaysia
  • Mohd Ibrahim Shapiai Malaysia–Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, 54100 Kuala Lumpur, Malaysia
  • A.K.M. Muzahidul Islam Department of Computer Science and Engineering (CSE), United International University (UIU), Madani Avenue, Badda, Dhaka 1212, Bangladesh

DOI:

https://doi.org/10.37934/araset.34.3.181191

Keywords:

Transfer Learning, EfficientNet-B0 model, Alzheimer's disease, MRI images, axial plane, multi-class classification

Abstract

Alzheimer's disease (AD) is an irreversible neurological disorder that causes the gradual decline of one's cognitive abilities, and thus, early detection is significant to slow down its deterioration. Magnetic resonance imaging (MRI) images have been commonly used to diagnose AD. Furthermore, deep learning techniques such as the Convolutional Neural Network (CNN) are utilized to assist the diagnosis due to the complexity of MRI's analysis. However, CNN models require large datasets for training and have a challenging nature for model optimization. Thus, Transfer Learning, an emerging method that can improve the performance of the deep learning model by eliminating the need for training from scratch, is introduced. This paper will propose a Transfer Learning-based EfficientNet-B0 model to classify MRI brain images for AD diagnosis. The MRI images are obtained from Alzheimer's Disease Neuroimaging Initiative (ADNI) database; only axial-plane images are used in this study. As a result, the multi-class classification of MRI images into AD, MCI, and NC classes using a Transfer Learning-based model resulted in a training accuracy of 98.93% and a validation accuracy of 87.17%. These results evidenced the significance of Transfer Learning in improving model performance.

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Author Biographies

Wong Pui Ching, Malaysia–Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, 54100 Kuala Lumpur, Malaysia

puiching1997@graduate.utm.my

Shahrum Shah Abdullah, Malaysia–Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, 54100 Kuala Lumpur, Malaysia

shahrum@utm.my

Mohd Ibrahim Shapiai, Malaysia–Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, 54100 Kuala Lumpur, Malaysia

md_ibrahim83@utm.my

A.K.M. Muzahidul Islam, Department of Computer Science and Engineering (CSE), United International University (UIU), Madani Avenue, Badda, Dhaka 1212, Bangladesh

muzahid@cse.uiu.ac.bd

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Published

2023-12-16

How to Cite

Wong Pui Ching, Shahrum Shah Abdullah, Mohd Ibrahim Shapiai, & A.K.M. Muzahidul Islam. (2023). Transfer Learning for Alzheimer’s Disease Diagnosis using EfficientNet-B0 Convolutional Neural Network. Journal of Advanced Research in Applied Sciences and Engineering Technology, 35(1), 181–191. https://doi.org/10.37934/araset.34.3.181191

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