Multiscale Deep Convolutional Networks for Characterization and Detection of Alzheimer's Disease using MR Images
Paper i proceeding, 2019

This paper addresses the issues of Alzheimer's disease (AD) characterization and detection from Magnetic Resonance Images (MRIs). Many existing AD detection methods use single-scale feature learning from brain scans. In this paper, we propose a multiscale deep learning architecture for learning AD features. The main contributions of the paper include: (a) propose a novel 3D multiscale CNN architecture for the dedicated task of AD detection; (b) propose a feature fusion and enhancement strategy for multiscale features; (c) empirical study on the impact of several settings, including two dataset partitioning approaches, and the use of multiscale and feature enhancement. Experiments were conducted on an open ADNI dataset (1198 brain scans from 337 subjects), test results have shown the effectiveness of the proposed method with test accuracy of 93.53%, 87.24% (best, average) on subject separated dataset, and 99.44%, 98.80% (best, average) on random brain scan-partitioned dataset. Comparison with eight existing methods has provided further support to the proposed method.

MR images

feature fusion and enhancement.

multiscale CNN

multiscale features

Alzheimer's disease detection


Chenjie Ge

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik

Qixun Qu

Student vid Chalmers

Irene Yu-Hua Gu

Chalmers, Elektroteknik

Asgeir Store Jakola

Göteborgs universitet

Proceedings - International Conference on Image Processing, ICIP

15224880 (ISSN)

Vol. 2019-September 789-793 8803731
978-1-5386-6249-6 (ISBN)

26th IEEE International Conference on Image Processing (ICIP)
Taipei, Taiwan,



Datavetenskap (datalogi)

Datorseende och robotik (autonoma system)

Medicinsk bildbehandling


Hälsa och teknik



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