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Proceedings Paper

An extended-2D CNN for multiclass Alzheimer's Disease diagnosis through structural MRI
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Paper Abstract

Current techniques trying to predict Alzheimer's disease at an early-stage explore the structural information of T1-weighted MR Images. Among these techniques, deep convolutional neural network (CNN) is the most promising since it has been successfully used in a variety of medical imaging problems. However, the majority of works on Alzheimer's Disease tackle the binary classification problem only, i.e., to distinguish Normal Controls from Alzheimer's Disease patients. Only a few works deal with the multiclass problem, namely, patient classification into one of the three groups: Normal Control (NC), Alzheimer's Disease (AD) or Mild Cognitive Impairment (MCI). In this paper, our primary goal is to tackle the 3-class AD classification problem using T1-weighted MRI and a 2D CNN approach. We used the first two layers of ResNet34 as feature extractor and then trained a classifier using 64 × 64 sized patches from coronal 2D MRI slices. Our extended-2D CNN proposal explores the MRI volumetric information, by using non-consecutive 2D slices as input channels of the CNN, while maintaining the low computational costs associated with a 2D approach. The proposed model, trained and tested on images from ADNI dataset, achieved an accuracy of 68.6% for the multiclass problem, presenting the best performance when compared to state-of-the-art AD classification methods, even the 3D-CNN based ones.

Paper Details

Date Published: 16 March 2020
PDF: 7 pages
Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113141V (16 March 2020); doi: 10.1117/12.2550753
Show Author Affiliations
Mariana Pereira, Univ. Estadual de Campinas (Brazil)
Irene Fantini, Univ. Estadual de Campinas (Brazil)
Roberto Lotufo, Univ. Estadual de Campinas (Brazil)
Leticia Rittner, Univ. Estadual de Campinas (Brazil)


Published in SPIE Proceedings Vol. 11314:
Medical Imaging 2020: Computer-Aided Diagnosis
Horst K. Hahn; Maciej A. Mazurowski, Editor(s)

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