Transfer Learning and Class Decomposition for Detecting the Cognitive Decline of Alzheimer Disease

• URL: http://arxiv.org/abs/2301.13504v1
• Date: Tue, 31 Jan 2023 09:44:52 GMT
• Title: Transfer Learning and Class Decomposition for Detecting the Cognitive Decline of Alzheimer Disease
• Authors: Maha M. Alwuthaynani, Zahraa S. Abdallah, Raul Santos-Rodriguez
• Abstract summary: This paper proposes a transfer learning method using class decomposition to detect Alzheimer's disease from sMRI images. The proposed model achieved state-of-the-art performance in the Alzheimer's disease (AD) vs mild cognitive impairment (MCI) vs cognitively normal (CN) classification task with a 3% increase in accuracy from what is reported in the literature.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Early diagnosis of Alzheimer's disease (AD) is essential in preventing the disease's progression. Therefore, detecting AD from neuroimaging data such as structural magnetic resonance imaging (sMRI) has been a topic of intense investigation in recent years. Deep learning has gained considerable attention in Alzheimer's detection. However, training a convolutional neural network from scratch is challenging since it demands more computational time and a significant amount of annotated data. By transferring knowledge learned from other image recognition tasks to medical image classification, transfer learning can provide a promising and effective solution. Irregularities in the dataset distribution present another difficulty. Class decomposition can tackle this issue by simplifying learning a dataset's class boundaries. Motivated by these approaches, this paper proposes a transfer learning method using class decomposition to detect Alzheimer's disease from sMRI images. We use two ImageNet-trained architectures: VGG19 and ResNet50, and an entropy-based technique to determine the most informative images. The proposed model achieved state-of-the-art performance in the Alzheimer's disease (AD) vs mild cognitive impairment (MCI) vs cognitively normal (CN) classification task with a 3\% increase in accuracy from what is reported in the literature.

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