A Multi-Stream Convolutional Neural Network for Classification of Progressive MCI in Alzheimer's Disease Using Structural MRI Images

Early diagnosis of Alzheimer's disease and its prodromal stage, also known as mild cognitive impairment (MCI), is critical since some patients with progressive MCI will develop the disease. We propose a multi-stream deep convolutional neural network fed with patch-based imaging data to classify...

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Vydané v:IEEE journal of biomedical and health informatics Ročník 26; číslo 8; s. 3918 - 3926
Hlavní autori: Ashtari-Majlan, Mona, Seifi, Abbas, Dehshibi, Mohammad Mahdi
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 01.08.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Alzheimer's disease
Artificial neural networks
Brain modeling
brain-shaped map
Classification
Cognitive ability
convolutional neural network
Convolutional neural networks
Diseases
Feature extraction
Image classification
Magnetic resonance imaging
Medical imaging
multivariate statistical test
Neural networks
Neurodegenerative diseases
Statistical tests
Transfer learning
ISSN:2168-2194, 2168-2208, 2168-2208
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Shrnutí:Early diagnosis of Alzheimer's disease and its prodromal stage, also known as mild cognitive impairment (MCI), is critical since some patients with progressive MCI will develop the disease. We propose a multi-stream deep convolutional neural network fed with patch-based imaging data to classify stable MCI and progressive MCI. First, we compare MRI images of Alzheimer's disease with cognitively normal subjects to identify distinct anatomical landmarks using a multivariate statistical test. These landmarks are then used to extract patches that are fed into the proposed multi-stream convolutional neural network to classify MRI images. Next, we train the architecture in a separate scenario using samples from Alzheimer's disease images, which are anatomically similar to the progressive MCI ones and cognitively normal images to compensate for the lack of progressive MCI training data. Finally, we transfer the trained model weights to the proposed architecture in order to fine-tune the model using progressive MCI and stable MCI data. Experimental results on the ADNI-1 dataset indicate that our method outperforms existing methods for MCI classification, with an F1-score of 85.96%.
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ISSN:2168-2194
2168-2208
2168-2208
DOI:10.1109/JBHI.2022.3155705