Abstract
In Alzheimer’s disease (AD), the tissues and cells in the effected brain start to die gradually and as a result the brain starts to shrink in size and the patient starts to suffer from memory loss. It is a progressive neurological disorder, and a typical AD patient gradually starts to lose memory, the ability to speak, some limb movements and the ability to think clearly. Due to the complex and progressive nature of the disease, it is important to study its progression and to understand the Mild Cognitive Impairment (MCI) at every stage. This research is an attempt to use Deep Learning (DL) Algorithms to study the gradual progression of AD and develop a strategy for the classification of its stages. This research is an attempt to use advanced ML and DL techniques to advance the classification and prediction accuracy of the progression of AD, hence, to improve patient care. In previous studies, structural magnetic resonance imaging (MRI) was used to study the progression of the disease. In this study, dataset from Alzheimer’s Disease Neuroimaging Initiative (ADNI) is used to study the AD progression using ML and DL algorithms. In this dataset, MRI and PET images of the brains, the patients’ genetic information, test of cognitive ability, CSF and blood biomarkers are used as predictors of AD. This dataset has four different classes. In this study, the progression of AD is evaluated using three Convolutional Neural Network (CNN) Architectures with transfer learning. The proposed CNN architecture in this study is VGG16, ResNet50 and Dense201. The DenseNet201 model outperforms and has the accuracy of 0.95%.
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Mian, T.S. (2022). Stage Classification of Alzheimer’s Disease Using Transfer Learning. In: Saeed, F., Al-Hadhrami, T., Mohammed, E., Al-Sarem, M. (eds) Advances on Smart and Soft Computing. Advances in Intelligent Systems and Computing, vol 1399. Springer, Singapore. https://doi.org/10.1007/978-981-16-5559-3_10
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DOI: https://doi.org/10.1007/978-981-16-5559-3_10
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